The Proceedings of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), Volume 2

This paper presents Gaussian Process-based Feedback Linearization Control for quad-tiltrotor to compensate for aerodynamic uncertainty. Unlike the quadrotor platform, the quad-tiltrotor with wing shape is affected by aerodynamic force and moment, which cause unstable behavior in hover and transition modes. The proposed control scheme uses the Bayesian non-parametric method without prior knowledge to estimate the uncertainty with strong nonlinearity. First, we derive the mathematical modeling of the quad-tiltrotor, including an allocation matrix to consider the tilt-angle. Second, feedback linearization controller is designed by choosing desired target response model. Third, Gaussian process-based feedback linearization is proposed to enhance stable flight performance under situations of uncertainty. Lastly, numerical simulation is performed to compare proposed controller with feedback linearization with integral action.

Peregrine falcon can perform stable gliding without a vertical tail. This paper assumed that the spanwise distribution of wing dihedral angle can affect the lateral-directional dynamic stability of Peregrine Falcon. The dynamic model of gliding Peregrine Falcon was built and the model was considered to be rigid. Thus, the falcon model can be treated as a fixed-wing aircraft. Its lateral-directional dynamic stability was analyzed based on linearized small perturbation theory under different spanwise distributions of wing dihedral angle. The product of natural frequency and damping ratio was used to assess the characteristic of the Dutch roll mode, and the eigenvalues were used to evaluate the stability of the spiral mode and the rolling mode in this paper. The criterion detailed in piloted airplane flying qualities was used as the reference value. Results show that all of the analyzed configurations have a well-convergent rolling mode and the dihedral angle changing has only a slight effect on the rolling mode stability. Peregrine Falcon can obtain a good Dutch roll mode characteristic by changing its wing dihedral angle. Most analyzed configurations have divergent spiral mode and meet the level 1 criterion.

Assuring stability of the guidance law for quadrotor-type Urban Air Mobility (UAM) is important since it is assumed to operate in urban areas. Model free reinforcement learning was intensively applied for this purpose in recent studies. In reinforcement learning, the environment is an important part of training. Usually, a Proximal Policy Optimization (PPO) algorithm is used widely for reinforcement learning of quadrotors. However, PPO algorithms for quadrotors tend to fail to guarantee the stability of the guidance law in the environment as the search space increases. In this work, we show the improvements of stability in a multi-agent quadrotor-type UAM environment by applying the Soft Actor-Critic (SAC) reinforcement learning algorithm. The simulations were performed in Unity. Our results achieved three times better reward in the Urban Air Mobility environment than when trained with the PPO algorithm and our approach also shows faster training time than the PPO algorithm.

Fixed-wing aircraft experience disturbances known as turbulence while operating in the air. This airborne turbulence applies unwanted forces and moments to the aircraft and disturbs the movement of the aircraft, interfering with the presumed performance prescribed by the controller. A Nonlinear Dynamic Inversion (NDI) control law is especially prone to degradation, proportional to model uncertainties caused by turbulence. To solve this issue, a Nonlinear Dynamic Inversion Controller based on the Gaussian Process Regression (GPR) is proposed as a disturbance observer. With the help of the Gaussian Process regression, model uncertainties from the turbulence are estimated for the feed-forward term, to aid the performance of the NDI control law. The Dryden model is one of many turbulence models that depict turbulence, in power spectral density form. This model was implemented in this paper to simulate turbulence, and the controller performance was evaluated.

This paper addresses the control problem of six-degree-of-freedom (6-DOF) multi-spacecraft formation flying in the vicinity of an asteroid under an undirected communication topology in the presence of uncertainties and disturbances. To cope with the issue that the real and dual parts of the twistor that represents the pose of a spacecraft are not in the same order, a scaled-twistor-based dynamic model is established for each spacecraft to avoid numerical difficulties in computation. On the basis of the scaled twistor model, a new distributed sliding surface is devised for the multi-agent system with a connected undirected communication graph. Then, a distributed control scheme is developed by combining the backstepping technique and sliding mode control theory under the assumption that the inertial parameters of the spacecraft and the upper bound of the external disturbances are known. To adapt to the uncertain inertial character of the spacecraft and external disturbances, adaptive laws are designed to estimate the inertial parameters and the upper bound of the disturbances to substitute for the corresponding assumed values in the controller. Finally, the stability of the whole closed-loop system is proved via Lyapunov stability theory. Numerical simulations are carried out to demonstrate the effectiveness of the proposed scheme.

Conservation of momentum and environmental robustness are achieved for the quad mass MEMS gyroscope in both the driving mode and the sense mode. A new type of all-silicon circumferentially distributed quad mass MEMS gyroscope is proposed, and the performance test is completed through the technology process. The four proof masses are distributed surround the central coupling ring symmetrical. Each proof mass has the same amplitude and oppositely phase with neighboring one. The angle rate could be measured by the sense comb difference capacitive signal. Moving structures of the gyroscope are fabricated by deep reactive ion etching, and then vacuum encapsulated by silicon-silicon fusion bonding at wafer level. Internal signals of the gyroscope are lead out through TSVs that fabricated by deep reactive ion etching and wet etching. A wafer-level test system is built, and a phase-locked amplifier was used to test the gyroscope Q value. The Q value can reach more than 300,000, the gyro's bias repeatability at room temperature is 4.44°/h, and the bias instability is 0.69°/h.

In most extant flight trajectory tracking control schemes, such as sliding mode control, gain parameter selection is time-consuming owing to the lack of explicit methods or rationales for the selection. Therefore, we herein propose a simple and efficient parameter selection method for sliding mode control. To demonstrate the effectiveness of the proposed method, we designed several types of trajectory tracking control laws for a rotorcraft. We derived the relation between gain parameters and desired damping ratio/desired natural frequency by utilizing modal analysis to transform trajectory tracking error as a second order system with respect to position and heading angle. Thus, we rationally determined gain parameters based on the expected system response, which corresponded to the classical response expected in a second order system. After implementing our method to the trajectory tracking control law for a Bo-105 rotorcraft in simulations, we determined that tracking performances can be upgraded or degraded based on damping ratio and natural frequency. In our controller design, as the desired damping ratio increased, although no significant differences exist at high damping ratio, undesired oscillatory responses disappeared. Further, the effects of variation in desired natural frequencies are not as significantly evident as those due to variation in damping ratio. However, the effects of desired natural frequency can be observed in the heading angle of the current trajectory tracking control design; as the natural frequency increased, the tracking accuracy increased. Therefore, the proposed method can serve as a guideline for tuning many parameters involved in the design of an advanced control law.

An autonomous flight system is essential for effective mission performance of UAVs, which are increasingly being applied in civil and military missions. Thus, in this study, an effective approach for implementing guidance and flight control systems is proposed. Based on the rapidly-exploring random tree (RRT) algorithm, the bidirectional potential quick (PQ)-RRT* is proposed to implement the path planner of the guidance system. The proposed bidirectional PQ-RRT* algorithm has a combination of three different types of improvement methods based on RRT*: potential field guided sampling (P-RRT*), modified RRT*(Q-RRT*), and bi-directional searching tree methods (Bi-RRT*). The convergence path was efficiently optimized using the line-of-sight path optimization algorithm. Then, a flyable trajectory was generated by a 7th order spline generator with waypoints from the path planner. Finally, incremental backstepping control was adopted to ensure trajectory-tracking performance within the overall operational flight envelope. To validate a series of processes, a simulation was performed to examine the practical realization. Based on the results, the bidirectional PQ-RRT* and line-of-sight path optimization algorithms were validated to provide an effective solution. In addition, the proposed flight control system exhibited excellent trajectory-tracking performance.

Urban Air Mobility (UAM) is a new type flying vehicle that transports people and goods around urban cities. The major electric vertical take-off and landing (eVTOL) concepts being studied and developed currently are 3-types, “Vectored Thrust”, “Lift + Cruise”, and “Wingless”. This paper focuses on the Wingless eVTOL because it has effective performances for short-range flight operation that will take the main role of Japanese UAM. Conventionally, a heavy rotor aircraft such as helicopter uses variable pitch to control thrust quickly, and heavy Wingless eVTOLs are suggested to have variable pitch as well. However, owing to the development of strong electric motors, the Wingless eVTOL like CityAirbus and VoloCity is controllable by variable speed method as well as UAV. The advantage of variable speed is significant regarding simplicity and maintenance cost. We analyzed the Wingless eVTOL detailed dynamics with rotor aerodynamics (blade element theory and momentum theory) and simple electric motor model. We took care of an additional counter torque by rotor inertia. UAV has the same effect, but it is so relatively small to air drag torque that it can be ignored. For the Wingless eVTOL, the angular acceleration of rotor brings large effect to control yaw. In order to overcome rotor inertia, we used many small propellers and the existing latest motor. This paper includes eVTOL dynamics that has unique behaviors and characteristics including both conventional helicopter and UAV. Moreover, our control architecture includes the command filtering, the reference model, the linear feedback and feedforward controller, the incremental dynamics inversion, and the mixer of 8 coaxial rotor with taking the rotor inertia. Finally, we applied the pilot command (attitude command) to the closed-loop system, and checked if hover and low speed requirements for small and moderate attitude change satisfy the ADS-33E.

This paper proposes a descriptor for the position and heading of aerial vehicles out of down-looking images using the concept of the visual semantic context aided by semantic segmentation and semantic labelled map which can be utilized during aerial navigation. The study presents the derivation of the visual semantic context from the given image while also conducting a feasibility analysis of the visual semantic context by presenting the corresponding error characteristics using both information- and heuristic-based residual metrics over the two contexts. The analysis using semantically segmented aerial images and the semantic labelled map indicates that the proposed concept shows distinct numeric features in a local sense and that it can be utilized as a position and heading fixing tool if associated with exhaustive search and/or data assimilation methods. The local uniqueness of the proposed context also implies that it is possible to use the concept as a validity index for a given aerial image when combined with a filtering paradigm.

In this paper we apply the commonly used model predictive control as the real-time optimal control for an aircraft arrival flight considering its nonlinear dynamics and constraints. Due to the technological improvement of optimization solvers, optimal control theory is applied in the Trajectory Based Operation analysis to achieve safe and efficient operations via trajectory optimization. In these applications, a proper formulation is required. For this work, we consider that the three-dimensional path constraint translated to the flight path coordinates uses the actual arrival route. Numerical simulation results demonstrate that the conventional aircraft can fly by the model predictive control from the initial position to the final position with decreasing the airspeed and altitude as a Continuous Descent Operation without violating the given constraints with or without the influence of the wind.

Formation flight of Unmanned aerial vehicles (UAVs) has become a research hotspot in recent years. How to plan a flyable path for UAV formation when flying with a given configuration to the destination safely is an important technology. Therefore, this paper proposes a path planning method based on Pythagorean Hodograph (PH) curves and Delaunay triangulation to generate a flyable reference path for UAV formation. Firstly, the kinematic constraints of the formation path are derived, while the formation moves. Secondly, the Delaunay triangulation and the Warshall-Floyd algorithm are used to obtain the best waypoints array from starting point to destination. Thirdly, the PH curves are applied to connect each two neighbour waypoints for meeting the kinematic constraints. The multi-population hybrid particle swarm genetic algorithm is proposed to generate an optimal flyable formation path. Finally, simulations are carried out considering a formation with three UAVs in a complicated environment. The simulation results show that the paths planned are connected by several PH curves, and all the paths can meet the kinematic constraints, avoid the obstacles and threat zones. Furthermore, the single population particle swarm genetic algorithm is also applied in the same simulation, and the simulation results show that the multi-population hybrid particle swarm genetic algorithm proposed in this paper has faster convergence speed and better stability.

An all-silicon comb tooth capacitive MEMS accelerometer with navigation potential is designed, fabricated and tested. Eight sets of variable-gap differential combs, four U-shaped springs and one frame-type sensitive mass constitute the sensitive structure of the accelerometer, which is fabricated by deep reactive ion etching (DRIE), and encapsulated by silicon–silicon fusion bonding at wafer level at normal pressure. Internal signals of the accelerometer are led out through through silicon vias (TSV) that fabricated by DRIE and wet etching. An analog application specific integrated circuit (ASIC) with double-terminal charge amplification interface and parameterizable PID is used to adapt the accelerometer. The MEMS chip and the ASIC chip are packaged in a ceramic shell finally. Test results show that the scale factor of the accelerometer is about 0.104 V/g, the bias stability and repeatability are better than 50 μg(1σ), the stability and repeatability of the scale factor are better than 40 ppm(1σ); the bias drift is about 3 mg and the scale factor change is about 2500 ppm between −40 and 60 ℃, and the vibration rectification error (VRE) is less than 40 μg/g2.

In this paper, the reentry guidance problem with no-fly zone constraints for a hypersonic vehicle is solved using the pseudospectral convex optimization method. The reentry guidance problem requires real-time guidance because of its complexity and uncertainty. Therefore, convex optimization is utilized, which has an advantage in computation time. Accordingly, the original problem composed of the nonlinear dynamics and non-convex constraints is transformed into a convex optimization form. This convexification process involves linearization and discretization. In this study, the pseudospectral method is used for the discretization process to improve the performance of the existing algorithm. Furthermore, some virtual terms are added to improve the convergence of the algorithm. Finally, we present numerical simulation results to demonstrate the validity of the proposed guidance algorithm.

Control flight into terrain (CFIT) refers to the fighter crash or serious damage and casualties caused by unknown terrain obstacles, fog and other bad weather or other operational errors, and the flight crew control the aircraft crashing into the mountains, crashing into the ground or flying into the water. In order to eliminate the control flight into terrain accident of fighter, an automatic ground collision avoidance system (Auto GCAS) is proposed. By matching the predicted flight trajectory with the current terrain, warning and maneuvering evasion instructions are issued to control the fighter to evade terrain. In order to adapt to various large attitude states and complex terrain environment, a method of automatic ground collision avoidance control and decision-making for fighter based on deep reinforcement learning (DRL) is proposed. Considering the limitation of parameter optimization method of deep reinforcement learning network with stochastic gradient descent, especially when it often falls into local optimal solution, it can not reach global optimal solution. Therefore, the genetic algorithm (GA), which conforms to the principle of survival of the fittest and survival of the fittest, is introduced into the optimization method of the parameters of the deep reinforcement learning network to obtain the global optimal solution.

With the development of more electric aircraft (MEA), the electrically powered actuators of electromechanical actuator (EMA) and electro-hydrostatic actuator (EHA) are becoming increasingly significant in flight controls. Generally, in the preliminary stage of EHA and EMA design, the performance analysis is always based on the linear models, the nonlinearities of inner actuator and load transmission are ignored, which results in inconsistent control characteristics, while the displacement output of the system is not expected and sometimes even unstable, and increase the difficulty of the regulation of control parameters. To figure out the effects of nonlinearities, the practical rules are proposed to easily and well conduct the stability analysis of actuation system. Aiming at the mentioned difficulties, this paper analyses the typical nonlinearities of electrical actuation system. EHA and EMA models are developed and the impacts on stability of each nonlinearities are analyzed. In order to reduce the influence of nonlinearities, controller parameters are optimized according to the practical rules. The whole research processes are summarized as the proposed method of analyzing the nonlinear effects on the stability, which combine the method of model-based system engineering (MBSE). These rules narrow the gap between virtual prototype and realistic system and makes the control design is much more convincing and more accurate.

A control allocation switching method is proposed for hexacopter control considering actuator faults. The proposed method is a two-stage switching scheme, consisting of a projection-based adaptive control allocation and a constrained optimisation-based control allocation. The proposed method switches control allocation algorithms from the adaptive control allocation to the optimisation-based control allocation when the actuator of the hexacopter is saturated. The switching criterion decides the switching timing so that the control allocation is robust for the delay of fault detection and isolation module. Numerical simulation demonstrates that the proposed method can effectively perform actuator-fault tolerant position control of the hexacopter without actuator saturation and consume less control effort compared with existing one-stage control allocation algorithms.

The goal of my paper is to create a new framework which provides sufficient semantic information for decision makings as a core component of applications such as SLAM (Simultaneous Localization And Mapping), robotics, AR (Augmented Reality), autonomous driving, etc.This framework does not provide dense point clouds. Rather, a scene is described with several features which the agent has been trained to recognize. Specifically, scenes are generated by extracting features from the space’s occupancy, the location of the light source, shape of the object, colors, and textures in the images. The extraction of the features is conducted by ensemble with deep-learning based feature extractors, traditional machine learning algorithms and image processing techniques. The deep-learning based feature extractors are trained by a supervised learning with data augmentation over 3D models; and the results of inferences are evaluated by a depth camera and retrained by unsupervised learning. Using existed methods it is difficult to utilize semantic information for unknown objects. But the agent in this methodology tries to describe them as much as possible by utilizing information trained in advance.For an odometry module, which estimates attitudes and positions, is implemented by a typical feature-based visual odometry methodology. The camera coordinate frame’s depth camera points and the pixel plane’s points are optimized by Levenberg–Marquardt algorithm after extracting a typical corner detection algorithm and tracking by an optical flow algorithm. Using several key-frames, sliding-windowed PnP (Perspective-n-Point), algorithms can be constructed. The visual odometry of the camera and the attitude estimation from the IMU (Inertial Measurement Unit) are loosely coupled. An ARS (Attitude Reference System) is built with a quaternion based linear Kalman filter, and mainly compensates the rotation error of the sliding-windowed PnP algorithm for each frame. The positions of the recognized objects are also included in the PnP algorithm to cover up the lack of features due to the lack of light or motion blur, which are a major problem in feature-based odometry. Since the re-recognized objects’ positions anchor the odometry, the drift problem which commonly occurs can also be solved.This framework performs a rough 3D reconstruction by interpreting the scene with minimal computing resources, and obtains the location of the agent. Therefore, it offers a simple 3D map and a graph structure, as a by-product for applications using this framework, and provides the attitudes and positions of agents for each frame. For applications that do not necessarily require dense geometric results, I propose that this framework can be utilized as a flexible and versatile component with fewer computer resources.

Hydraulic servo actuator (HSA) is a key component to drive flight control surface and widely used in today’s civil aircraft. Owing to the cumulatively long working, HSA will inevitably be some failures and cause performance degradation and even loss of control, these are increasing the cost of maintenance. In order to reduce the impact of this effect, this paper pay attention to the fault diagnosis technology, to find an effective and reasonable theoretical diagnosis method and finally support for the safe-critical design of flight control actuation system. The aviation jet pipe type valve-controlled hydraulic servo actuator is under study. A simplified mathematical model is firstly established. Then, an advanced HSA model is developed in the multidisciplinary simulation platform of AMESim, the simulated faults are injected in the functional model and simulation data are carried out. After that, the faults data are obtained from the sensors and fifteen fault features are extracted according to mathematical theory. Finally, the back propagation (BP) neural network is used for data training and test, the proposed method can well realize the fault diagnosis function of HSA. It can provide accurate reference for fault judgment of aeronautical actuator system and reduce fault maintenance cost.

Aiming at the typical faults of the electromechanical actuation system of a redundant aileron control surface of the large aircraft, this paper proposes a fault diagnosis method based on the combination of multi-agent and adaptive threshold (MA-AT) considering the redundant electromechanical actuation system-flight control system closed-loop, which tackles the difficult problem of the determination of the fault diagnosis threshold. Firstly, the composition of the electromechanical actuation system of large aircraft is analyzed and an accurate mathematical model of the electromechanical actuator with fault information of each component is established. Secondly, the homogeneous multi-agent model is established according to the information interaction among the redundant electromechanical actuators and the distributed relative output error vector is obtained. To minimize the estimation error, the gain matrix of the observer is solved by combining pole assignment and H-infinity performance multi-objective design method, and the design of fault diagnosis observer for redundant electromechanical actuation system is completed. Finally, combined with the flight quality requirements of large aircraft and the design of typical roll angle control mode control law, the signal threshold for fault diagnosis of the electromechanical actuation system is adaptively obtained from the influence of fault signal on the electromechanical actuator and the influence of electromechanical actuator performance parameters on the key state of the flight control system, which provides the judgment basis for fault isolation of electromechanical actuator.

This paper discusses the feasibility of a novel magnetorquer by illustrating its magnetic performance and comparing it with conventional magnetorquers. Unlike the traditional magnetorquers used in space, we adopted the electropermanent magnet as a source to generate torque. We focused on the fact that an electropermanent magnet doesn’t consume any continuous electric power supply to hold its magnetic property once it is magnetized. From this point of view, it is worthwhile to investigate further the characteristics of the electropermanent magnet and aim to replace the electromagnets in the magnetorquer. Furthermore, based on the current findings, we presume that this new type of magnetorquer has relatively low energy consumption while performing attitude control maneuvers, especially for detumbling and stabilizing a satellite. To proceed with the application, we developed a preliminary experimental setup and have identified some of the properties of the electropermanent magnet. We have discovered that it can switch on and off and alter the magnetic dipole moment, including its direction. Thus, in other words, we can say that it is programmable. At this stage, this property seems to be related to two aspects, the number of electric current pulses and the supply voltage driven to the winding wire, which is closely associated with the external magnetic field. These two parameters are correlated to each other, and both affect the magnetic strength and the transient profile in a different manner. Therefore, in this presentation, we aim to show the latest progress of our experiment regarding the characterization of the electropermanent magnet.

In this study, a fault tolerant controller is designed for a hexacopter with actuator faults using reinforcement learning method and extended state observer. The control system consists of an outer-loop controller, which is designed to track the horizontal position using a reinforcement learning scheme, and an inner-loop controller, which is designed to track the altitude and attitude using extended state observer and the baseline controller based on nonlinear dynamic inversion. Using integral reinforcement learning based on value iteration, the optimal control problem is solved for a partially known system model, with an assumption initial stabilizing control is not required. The disturbances due to the actuator faults are estimated by an extended state observer so that the generalized forces, the virtual controller, may be designed. An optimization-based constrained control allocation method is adopted to allocate the desired generalized forces to six rotors considering the saturation of the actuators. Numerical simulation is performed to demonstrate the effectiveness of the proposed controller for a 6-Degrees of Freedom hexacopter system.

Model Predictive Path Integral (MPPI) control framework algorithms have been studied for use in autonomous control systems because they are convenient to implement using model predictive trajectory samples with a stochastic control approach. They can also deal extensivlely with complex desired costs and constraints. This paper presents a path following control algorithm based on the model predictive path integral control framework for autonomous vehicles. By using the importance sampling method in the model predictive control, the iterative path integral provides acceleration commands for a vehicle, allowing it to track a virtual target on a desired path and achieve the optimal trajectory under the constraints. The optimal acceleration commands are updated using a stochastic control approach using model predictive trajectory samples. This approach allowed us to efficiently solve the nonlinear control problem with complex costs and constraints, without intractable convexification or linearization. We implemented the Graphics Processing Unit (GPU) algorithm to show that this algorithm can quickly compute this problem. We tested the algorithm on various paths and under wind disturbance, using a nonlinear disturbance observer that allowed us to predict the model more correctly in an uncertain environment. The simulation results show that the algorithm is effective and applicable to path-following guidance for various paths under disturbances.

Multidisciplinary optimization in aircraft design is approaching its limits, and breakthrough technologies are needed to improve further fuel efficiency. If in-flight switching control can be feasible between a drag-minimizing load distribution during a cruise, which accounts for most of the flight time, and a dedicated gust-tolerant load distribution for turbulent airspace, then it will be possible to extend the aspect ratio of the main wing while mitigating actual structural damage without weight increase. The technology to design the optimum target load distribution by multi-objective optimization already exists, but the feasibility of feedback control to realize the target load distribution has not yet been investigated. In this study, this problem is solved by an adaption scheme consisting of in-flight pressure field sensing, real-time modeling of unsteady pressure fluctuations, and stochastic optimal control. The effectiveness of the proposed active technology will be shown through wind tunnel testing.

With the increasing number of unmanned aircraft flying beyond visual line-of-sight missions, it becomes more and more important to consider and the safety and efficient use of airspace shared between unmanned and manned aircraft. To enable fast-time simulations for the evaluation of safety and efficiency in several future scenarios, representative models of manned aircraft operations at low altitude are needed. In this paper we introduce the main missions flown at low altitudes by manned aircraft and show how we modeled media helicopter operations from big data. We describe the algorithm we developed to automatically extract the model parameters from actual flight tracking data in detail and present the resulting stochastic distributions of the model parameters. We also clarify the assumptions and limitations of our current analysis and show directions for further development.

With the improvement of automation, the complexity of aircraft systems is increasing, resulting in the difficulty of finding out the real cause for a multitude of fault symptoms originating from different aircraft systems. Once a failure arises during system operation, it may cause great economic losses and even catastrophic consequences. A critical effort to maintain aircraft systems and enhance aircraft safety is the development of Onboard Maintenance System (OMS). As vital parts of the OMS, reasoning techniques play a crucial part in realizing real-time and more accurate fault diagnosis. Therefore, the research on reasoning techniques for OMS is of great significance. This paper is focused on the reasoning techniques, which belong to the field of Artificial Intelligence, and their applications in OMS. First, reasoning strategies are described. Second, a comprehensive review of different reasoning methods, such as knowledge-based reasoning, model-based reasoning, data-driven reasoning and hybrid reasoning, is given. A number of practical issues in applying these methods in the field of aerospace are explored, respectively. Besides, the advantages and the disadvantages of each method are discussed. Third, some architectures for reasoning are presented. Then, the reasoning approach applied in OMS of Boeing 777 is outlined. Lastly, a summary of this review is given, the recommendations for further research on development of reasoning techniques used in OMS are made.

The flight dynamics of a spaceplane strongly depends on the flight environment, including the dynamic pressure, angle of attack, and Mach number. In addition, spaceplanes fly at pitch angles around 90° during powered and coasting ascent, and Euler angles are known to be coupled with roll and yaw angles at such pitch angles. In this paper, the control system for a spaceplane based on dynamic inversion theory is proposed. To avoid the problem of the singularity of Euler angles, the equation of state is written in terms of quaternions. The control laws are designed and evaluated in the following three cases: (1) The case where only the attitude error calculation of the control law using the Euler angle is modified. (2) The case where the vector part of the quaternion is used as the control variable. (3) The case where the vector part of the error quaternion is used as the control variable. Finally, the results of the numerical simulation show that Case (3) has the best control performance.

Aimed at defects such as low efficiency, lengthy request-response processes, and proneness to collateral damage associated with the traditional map/voice-guided CAS [1], a new design of an Information Integration Close Air Support (ICAS) system is proposed here. The functional requirements of the ICAS system are analyzed to present the overall framework. The software architecture of the ICAS system is devised, and the principle and implementation of the software’s core functions are studied. In addition, the workflow of the ICAS system is presented. Finally, the feasibility and effectiveness of the ICAS system are proved in a simulation. The method provided in this paper defines the situation information sharing among various combatants and allows ICAS missions to be completed more rapidly and more accurately.

Tilt rotor aircraft is a type that has gained wide attention and developed quickly in recent years, combining the advantages of rotorcraft and fixed-wing aircraft, such as vertical takeoff/landing ability and high-speed cruise. An extended dynamics equation, based on six degree-of-freedom (6-DOF), is proposed in this paper, to solve the problem of inertial properties dynamic behavior during transition. Then, the transition corridor is calculated considering force and torque balance based on nonlinear programming. Last, the study focuses on the transition flight of the tilt rotor aircraft with distributed propulsion, which is nonlinear, strongly coupled and uncertain. And due to the significant change in dynamic characteristics, the error in the aerodynamic calculations and the strong external interference. An attitude controller is designed, by Active Disturbance Rejection Control (ADRC) method, as an inner loop control demonstrating excellent dynamic suppression performance. Overall, distributed electric propulsion not only presents unique control challenges, but also provides new solutions for transition flight control. This paper focuses on new properties brought by distributed propulsion to the transition envelope. Furthermore, a robust control method is designed, which may be a new research approach to the tilt rotor aircraft with distributed electric propulsion.

By raising a practical problem in missile interception guidance with detailed system dynamic equations, along with several constraints on initial states and terminal states, the optimal control problem will be converted to a parameter optimization problem by using implicit integration Hermite-Simpson method, which approximates states and control using piecewise continuous polynomials to simplify the involved integration calculations, will be used to discretize the time domain with steps of duration. Furthermore, augmented Lagrangian method (ALM) will be introduced to convert constrained problem (i.e. primal problem) into unconstrained one (i.e. barrier problem) and penalty term will be added to cost function. Meanwhile, particle swarm optimization (PSO) will be applied to optimize the parameter vector, including system states, control inputs and Lagrangian multipliers, evaluating the cost function at each iteration during the simulation until reaching the minimum value of cost function, which also suggests that the particles are converged, and the parameter vector is optimized. The main contribution of the study is to propose a hybrid optimal solver for the constrained optimization problem, an augmented Lagrangian particle swarm optimization (ALPSO), and comparison results between the self-build ALPSO solver, MATLAB built-in fmincon function, and the theoretical ones, verifying the correctness of the self-build solver for constrained optimization problems with accuracy of 15.49 m maximum error during the whole one thousand meters of flight.

Aiming at the construction of flight conflict network under the condition of free flight, traditional methods usually take the position distance relationship between two aircraft as the standard of conflict judgment, which lacks aircraft heading information, can't judge potential conflict in time, and easily leads to conflict false alarm. Therefore, based on the traditional flight conflict network, this paper constructs an “ellipsoid” protected area, proposes a three-dimensional velocity obstacle model, and introduces it into the complex flight situation network for conflict judgment. Compared with position model, cylinder model and sphere model, the advantages of ellipsoid model based on velocity obstacle method are expounded. The conclusion shows that the false alarm rate of ellipsoid model is 39.21%, 12.17% and 55.66% lower than that of position, cylinder and sphere model; the modeling method can effectively reduce the redundant edges of the conflict network and the false alarm probability of the conflict; after adding visual elements, the conflict network can more accurately and intuitively reflect the complex situation of airspace, and improve the work efficiency of controllers.

The automatic landing guidance technology which meets the CAT III standard is the key technology to realize all-weather accurate landing, and it is an effective measure to reduce the accident rate in approach and landing stage. This paper proposed a generation of guidance errors and guidance instructions for automatic landing based on GPS-Aided ILS. Firstly, the glide path was defined by key points, and the mathematical description of the glide path was given in the form of vector. Then, a landing guidance errors calculation algorithm based on the spatial geometric relationship and a variable gain guidance instructions generation method based on the distance strategy were proposed. Finally, five hundred times stochastic simulations were conducted to evaluate the proposed guidance law under the condition of wind and sensors error disturbance, with the flight technical error, angle of localizer deviation and angle of glide slope deviation as indexes. It can be drawn from the results that the landing accuracy of the aircraft guided by the guidance algorithm given in this paper can meet the CAT III standard.

Shinshu University has been developing a computer simulation-based navigation algorithm verification system. The system performs the final confirmation of a subject navigation algorithm by executing the flight test items specified in Table 2–9 of the RTCA DO-334 MOPS by computer simulation of an aircraft’s flight along a reference trajectory. Generating a high-precision reference trajectory requires many aircraft parameters and is computationally intensive. To alleviate these burdens, we developed a program that generates a simplified trajectory from takeoff to landing that includes turning maneuvers. In this paper, dynamic motions such as pitch up/down and lateral-directional motions such as steady sideslip, which are required in DO-334, were newly incorporated into the trajectory generation program, and inertial navigation and GPS navigation were performed along the output trajectory to confirm the usefulness of the verification system. The obtained navigation outputs were consistent with the characteristics of true data, and confirmed that the influence of flight test maneuvers on the navigation could be evaluated. It was therefore demonstrated that the system can simulate the DO-334-based verification of a navigation algorithm.

Air traffic flow management initiatives are important to balance demand and capacity. In Japan, in addition to already practiced ground delay programs, congestions at the busiest domestic airport, i.e. Tokyo International (Haneda) Airport, are to be tackled by controlled enroute delays and structured sequencing arrival flows via a point merge system (PMS). The objective of the current research is to evaluate the effect of various traffic flow management control parameters on ground delay, airborne delay and throughput. The evaluation is performed using a joint traffic flow-PMS numerical simulation, developed based on real air traffic radar data (CARATS Open Data available from Japan Civil Aviation Bureau). The PMS simulation allows for the analysis of airborne delay absorption distribution in different airspaces as well, focusing on the approach control area and arrival sector, connecting the enroute and approach control area. Research results show that flights which are subject to ground delay also experience airborne delay, generally within the allowed margin set by the air traffic flow management control parameters. Lost throughput is rare, which implies that the air traffic flow management control parameters can be further optimized to reduce airborne delay without losing runway throughput.

With the development of science and technology, functional crosslinking and resource sharing have become the common characteristics of large complex systems. At the same time, it also brings the challenges of fault diagnosis and location, such as the enhancement of system fault correlation, implicit spread and chaos. As a result, the traditional model is not sufficient to describe the functional failure mode of complex crosslinking systems. In this paper, a metamodel-based complex crosslinking system modeling method is proposed and applied to build the model for airborne complex crosslinking system. The influencing factors and relationships between system functions and faults are studied, and a metamodel-based fault example of airborne complex crosslinking system is realized.The application of metamodel technology to the establishment of airborne complex crosslinking system model can make the subsystem model achieve a higher level of abstraction. The good general performance of metamodel solves the problem of complex system model reuse, provides quantitative support for subsequent development, and simplifies the correlation characteristics of airborne complex system crosslinking function. It provides the foundation of domain model for simulation, design and development of subsequent systems.

A method to generate sequential obstacle avoidance trajectories for Unmanned Aerial Vehicles (UAV) is proposed. The proposed Predictive Artificial Potential Field (P-APF) method can generate smoother and less time-delayed avoidance trajectories in real time while maintaining the same high avoidance performance and low computational load as the conventional APF method. It is expected that the use of UAVs in transportation systems will increase, and the proposed P-APF method is highly effective in situations where multiple UAVs perform respective operations in the same flight envelope. The P-APF method generates a potential field and repulsion vectors based on current information and also on predicted future UAV trajectories and observed obstacles. Thus, since prediction is incorporated into the trajectory generation process, the proposed method can generate trajectories that start avoiding obstacles earlier than the conventional APF method. To evaluate the performance of the proposed method, simulations of a UAV avoiding static or dynamic obstacles were conducted using the proposed method and the conventional method. The results confirmed that the P-APF method can maintain safety and take evasive action with a smaller delay than the conventional method.

With the rapid development of power-by-wire technology applied in flight controls, electro-hydrostatic actuator (EHA) and electromechanical actuator (EMA) are preferred to the application of primary flight controls. Electrical trimmable horizontal stabilizer actuator (THSA) is a novel type of actuator with the function of fault tolerance that is different from common EMA. THSA can avoid mechanical path failure and prevent the load from driving backward and impacting the power grid by utilizing no-back mechanism. This paper focuses on the study of no-back mechanism by introducing a novel type of friction disc of skewed roller friction disc (SRFD). Firstly, the mathematical model of the no-back device and the SRDF is introduced, and the friction performance under different working conditions is studied based on experimental ways. Then the realistic no-back model is established and the friction coefficient data from the test is obtained. The simulation results show that this model is reliable, and the model can be expected to be applied in THSA system-level model.

The on-board orbit determination precision of geostationary orbit satellite based on orbit prediction results of on-ground is dependent on on-board orbit re-generation algorithm. Also, there is a limit in that the error may increase when communication with the ground station cannot be established for a long time. Recently, as the use of GNSS has become possible even in geostationary orbit satellites, real-time orbit determination using the navigation solution of the GNSS receiver has become possible. The conversion error generated in the ECEF to ECI coordinate transformation process, when using the navigation solution of the GNSS receiver, affects the orbit determination error. In addition, there may be cases where the use of GNSS is temporarily difficult in geostationary orbit. Considering these points, in this paper extended Kalman filter based orbit determination algorithm that use the navigation solution of the GNSS receiver. To minimize the orbit transformation error, the post-processed orbit navigation solutions calculated using Earth Orientation Parameters are used as measurement values. The simulation results show that the orbital determination error is finally reduced by applying the precise coordinate transformation, and stable orbit determination is possible even when the use of GNSS is temporarily difficult by applying the proposed algorithm.

Recently, urban air mobility (UAM) has been attracting a lot of attention as a possible solution to solve traffic congestion in metropolitan city areas. However, there are still several problems of UAM operation to be solved. One of the most important issues is safe flight under bad weather conditions. Especially, safe operation of the UAM under strong winds and turbulence is a critical problem. To address this issue, the flight characteristics of the UAM should be evaluated under an actual urban wind environment. In this study, a method of generating realistic urban wind environments is suggested. The weather research and forecasting (WRF)–large eddy simulation (LES) coupled model with the iterative nesting algorithm was adopted to generate a wind environment with sufficient resolution for UAM. To validate the WRF accuracy, the result was compared with observation data. The LES results were also analyzed in terms of the characteristics of the wind pattern around the building.

In this paper, two basic aspects of multi-rotor UAV, the air braking capability and obstacle avoidance function, are analyzed and studied on a certain type of fire-fighting UAV. Firstly, based on the motion model, the force analysis of the UAV is carried out to explore the factors that affect the braking ability; the mathematical relationship is established between the influencing factors and the braking distance, and the braking problem is solved in PYTHON. After that, the obstacle avoidance function of the multi-rotor UAV is developed based on the ROS environment. Several commonly used obstacle detection technologies and path planning algorithms are introduced and their advantages and disadvantages are compared. Under the ROS environment, simulating the advanced obstacle avoidance strategy according to VFH algorithm, and using PX4 external control mode in Gazebo simulation environment to verify the feasibility of the designed obstacle avoidance strategy. The results show that in actual flight, when the braking pitch angle is 30°, the UAV can slow down and hover within a safe distance of 10 m, meanwhile the advanced strategy based on VFH algorithm can be realized in simulation environment.

Runway use has strong impact on the operational efficiency especially in the congested airport with multiple runway configuration. The Tokyo International Airport, which is the most congested airport in Japan, has four runways. Air traffic controllers assign either of two runways to aircraft departing from and arriving at the airport. Take-off and landing times influence each other where departure and arrival aircraft use the same runway. Also, some time intervals are necessary when aircraft departing behind a landing aircraft on a crossing runway. Hence, considering both departure and arrival aircraft is indispensable to obtain the optimal runway assignment in terms of efficiency. In this study, we examined optimal arrival runway assignment that minimized the sum of departure and arrival queuing times. The computation revealed that air traffic controllers reduced queuing time compared to the nominal runway assignment determined on the basis of approaching directions. Furthermore, the optimal runway assignment can reduce more queuing time.

This paper presents a Legendre pseudo-spectral method for solving trajectory optimization problems with realistic state and input constraints. Fuel mass flow rate, rate of bank angle, amount of fuel, and angle of attack as considered as constraints. The final flight path angle and minimum final velocity constraints are added for a high probability of interception and maneuverability. Lift/drag coefficients and specific impulse which are functions of Mach number, angle of attack, and altitude, were handled with the lagging technique. These parameters are fixed at the current optimization iteration and updated at the next iteration. The original problem is converted to a free-final time problem using a time scale variable, which is an additional optimization variable. In the Pseudo-Spectral method, states and inputs are approximated with the Lagrange polynomial at every flipped Legendre–Gauss-Radau (LGR) collocation point. Dynamic constraints and other constraints are transcribed to algebraic equations at these LGR points. This discretized problem is solved with a MATLAB optimization solver, and values of states and inputs at every LGR collocation point are obtained. The Pseudo-Spectral method is proven to be effective in solving a trajectory optimization problem having realistic constraints, and is robust in solving trajectory optimization problems with a crude initial guess. A numerical simulation validates that this method produces accurate results with a small number of node points.

In this paper, we propose an anomaly detection method for missile simulation data utilizing a clustering-based algorithm. In the proposed method, to assess the performance of the control system, the parameters related to the missile’s guidance and control are selected from simulation data for the automated detection of anomalous cases. Then, the selected data is sampled at fixed intervals to match the analysis scope and reject unnecessary information. The feature points for each parameter are chosen in a way that emphasizes abnormal patterns in time series data. Finally, the feature vector of each data sample is formed by combining all feature points. In data analysis, DBSCAN, an unsupervised clustering algorithm, is applied to detect abnormal simulation data in the feature space. In this work, the proposed method was tested with a 6DOF surface-to-surface guided-missile simulation dataset. The results indicate that the proposed method can be used to automatically identify simulation data that reflects abnormal behavior in the control history, which implies the method’s potential to assist the process of finding critical simulation data without predefined criteria or training data.

The Electronic Navigation Research Institute and Korea Aerospace University have proposed an initial Free Route Airspace (FRA) concept for the Fukuoka and Incheon Flight Information Regions (FIR) to improve air traffic flows and air traffic management in northeast Asia. We are now working to elaborate the concept, quantify benefits, and identify implementation issues. This paper examines two air traffic flows in Fukuoka FIR: (1) Japanese domestic flights between the highest traffic city pairs, and (2) overflight traffic between Korea and North America across radar-controlled airspace. From an analysis of operations based on flight plan and radar data for 2019, prior to the COVID-19 pandemic, FRA design and implementation issues are considered. Our analysis and findings are expected to contribute to the planning of FRA implementation in Northeast Asia.

Tilt-rotor UAVs are exceptionally suitable for near-ground unmanned transportation and other fields because of their helicopter mode, transition mode and fixed-wing mode. However, the tilt-rotor transition on trajectory planning presents a challenge, as performance constraints of different modes must be considered and a trajectory planning algorithm is required to optimize a collision-free and short trajectory with low fuel consumption and small cost. To address these challenges, this paper adds performance constraints of the tilt-rotor UAV to the hybrid algorithm by combining the A* algorithm and the artificial potential field method; meanwhile, the rationality of trajectory planning is improved by setting the tilt-rotor transition region separately in the global. Simulation results show that a reasonable trajectory can be planned by setting the tilt-transition region by itself and using a hybrid algorithm.

Regarding the flight trajectory of a suborbital spaceplane, considering the situations such as abort flight, it is difficult to set the reference trajectory in advance. Therefore, a guidance algorithm is required for the suborbital spaceplane to generate the optimal trajectory flexibly during a return flight. Evolutionary algorithms that can search for solutions globally are effective methods for this purpose. Based on this idea, a return trajectory generation method using a dynamic distributed genetic algorithm (DynDGA) has been proposed. In this study, three improvements to the previously proposed DynDGA-based method are presented. First, the continuity of the angle-of-attack and bank-angle commands before and after the optimal trajectory update was ensured. Second, the angle-of-attack command satisfied the trim condition. Third, fitness was calculated based on hierarchical fuzzy logic. The effectiveness of the improved method was confirmed by trajectory generation simulation of a return flight of a suborbital spaceplane.

In this research, the feasibility of a reusable delta winged vehicle that performs sub-orbital flight up to an altitude of 100 km is examined by a three-degree-of-freedom (3-DOF) simulation as an initial study. In the case of a conventional aircraft, the effect of control surface deflection is often ignored in simple 3-DOF simulations because the deflection angle is small, but with a delta wing configuration, the control surfaces must generate higher forces because of the lower distance between the control surface’s center of pressure and the vehicle’s center of gravity. When a control surface is deflected in a 6-DOF simulation, it directly generates a force that affects the vehicle’s path, and the path may differ significantly from the case of a 3-DOF simulation with the control surfaces fixed at zero angle. Therefore, we decided to perform a 3-DOF simulation in which the control surfaces were set to around the trimmed angle. Studies so far have confirmed that the vehicle can fly up to an altitude of 100 km.

In recent years, the application demand of UAV has been increasing rapidly. In the face of complex tasks, the mission capability of a single UAV is limited. Comparatively, multi UAV formation can greatly improve the efficiency, reliability and redundancy. This paper presents a multi UAV formation method based on ROS system. Firstly, several UAV formation methods are introduced. With the comparison of these methods, the distributed formation method is adopted. Then, the software architecture of UAV formation system is designed based on ROS system. And on this basis, the estimation node, control node, formation node and display node are designed. By subscribing to Px4 flight control node, the estimation node obtains the UAV status, position, speed and Euler angle. Then the estimation node converts the message format and publishes them for other nodes to use. The control node subscribes to the formation control command of the formation node, estimates the UAV state information of the node, calculates the expected speed of the UAV through the previously adopted formation control algorithm, and sends the message to the Px4 control module. In addition, the control node establishes contact with the control module to realize the functions of unlocking, locking, takeoff and landing. The formation node can calculate the relative deviation of UAV formation position according to different formation requirements. The display node is used to display the current status of UAV, including position, speed, yaw angle, etc. Finally, the feasibility of the formation control method is verified in the Gazebo simulation environment, and the results show that the method can realize formation flying, formation transformation and maintenance. The above contents lay a certain foundation for the future related research and can be used for reference.

In this paper, a design for a fan-in-body compound rotorcraft with tandem ducted fans, wings, and two pairs of tractor propellers is proposed. It operates either as rotorcraft or a fixed-wing aircraft. This paper presents initial results on the performance characteristics and flying qualities of the fan-in-body compound rotorcraft in transition flight. A flight simulation model of the fan-in-body compound rotorcraft was developed. The power required for various settings of the flap angle, fuselage attitude, and control allocation were evaluated for further analysis of transition flight. Longitudinal axis and lateral/directional SCAS controllers were designed. Flying qualities were evaluated for the aircraft with control allocation in forward flight using CONDUIT. The evaluation index was decided through ADS-33 and MIL-STD 1797A. Robustness analysis was performed by applying a random perturbation to the state variables and control inputs, and the robustness of the control input allocation algorithm was verified.

Low Earth Orbit (LEO) satellites are designed to have orbital characteristics such as sun-synchronous orbit, frozen orbit, and repeat ground track depending on the mission of the satellite and the performance of the payload (e.g. camera, radar). Among them, the repeat ground track is useful when periodically observing a specific area. However, LEO satellites are affected by the Earth’s atmospheric drag and their altitude decreases. As a result, the ground track of the satellite drifts from the reference ground track. Also, since Earth’s atmospheric density is affected by solar activity, the satellite’s altitude does not decrease uniformly. In this study, a repeat ground track maintenance simulation is performed considering solar activity. The mean orbital elements obtained through the simulation are converted into osculating orbital elements to compare and analyze the differences.

Path planning is a key technology for Unmanned Aerial Vehicles (UAVs) to complete the operational mission in a complex battlefield environment. A step-by-step path planning method based on the Layered Double Deep Q-Network with Prioritized Experience Replay (Layered PER-DDQN) is proposed in this paper. The novel method is constructed by combining the threat avoidance network and collision-free network based on the PER-DDQN framework. By analyzing the current environment of the UAV, the networks output threat avoidance action vector and obstacle avoidance action vector, and the method does a weighted summation of the action vectors according to the weight of the subproblems to obtain the final action. The simulation experiment verifies that the Layered PER-DDQN path planning method has better convergence and practicability than the Deep Q-Network and A* algorithm.

According to the Ministry of Land, Infrastructure, Transport and Tourism, in Japan, the number of small aircraft accidents/incidents is larger than the number of large aircraft accidents/incidents. The reason why there are so many small aircraft accidents/incidents is the higher incidence of human errors, and research on accident/incident prevention is not progressing when compared to large aircraft. The purpose of this research is to aim to reduce the number of these accidents/incidents caused by human error. As a specific goal, we aim to develop a Head Mounted Display (HMD) system that recognizes human error and provides a visual cue to the pilot when it occurs. This system identifies that human error has occurred when an operation lever is not in the correct position, determines the content of the warning and displays it. The correct position of the lever is determined for each flight phase, such as take-off, cruise and landing. Therefore, this system requires the technology to recognize the status of the lever, to determine the active flight phase, to estimate whether or not an error has occurred based on these outputs, and to then set and display appropriate warnings. This time, we describe the outline of the HMD system and the development status of the individual technologies described above.

Currently, Mars Airplane Balloon Experiment Two (MABE-2) is planned to acquire the aerodynamic data in the stratosphere, an atmospheric environment similar to Mars. This paper presents the design and performance evaluation results of the control system for this flight experiment. The control gain of this system was designed using the statistical optimization method and evaluated by Monte Carlo simulation. The performance evaluation results of this control system were obtained by independent error analysis with various prescribed errors individually and by an Influential Input Parameter Detection method.

Electromechanical actuator (EMA) is a servo mechanism that controls the flight vehicle in the aerospace field and it has the main advantages of high operating efficiency and easy maintenance. At present, most EMA performance simulation analysis are based on deterministic parameters. In fact, as the working environment and operating conditions change, some inner parameters may be changed and become uncertainly. In this paper, a novel method of EMA performance based on uncertain parameters is proposed to solve the problem that the existing deterministic models are difficult to evaluate the actual performance of EMA. Firstly, a deterministic model is built on the AMESim simulation platform. Then, the probability analysis software NESSUS was used to consider the uncertainty of parameters, and the co-simulation model was established. Finally, the influence of parameter fluctuation on EMA system performance is studied by using this method. Simulation results verify the correctness of the proposed method. Compared with the traditional simulation method based on parameter determination, the model built by this method has higher precision, and the simulation results are closer to the real EMA prototype.

“The Roadmap for the Application and Technology Development of UAVs in Japan” classifies the flight operations of unmanned aerial vehicles (UAVs), more popularly known as drones, into four levels, the highest of which is “beyond visual line of sight operations without assistants in populated areas.” This study performed a risk assessment of Level 4 flight operations. Using failure mode and effects analysis, we identified the loss of the global positioning system (GPS) signal as an important failure mode. When the GPS signal is lost, drones are often designed to enter the ATTI (attitude) mode, a manual control mode, but this mode supports only altitude maintenance and is susceptible to environmental factors. We conducted an experiment using a drone flight simulator to observe the behavior of operators when the GPS signal was lost and examined possible risk factors to propose countermeasures. The participants (N = 21) performed five scenarios of delivery tasks around Akihabara Station, with the location of the GPS signal loss changing in each scenario. By analyzing the results of behavioral observations and interviews, we found that the behavioral patterns differed based on the information used by the participants, and they made decisions regarding the relationship between the distance to the destination and distance to the emergency landing points, among other options.

Trajectory Based Operations (TBO) require accurate aircraft performance estimations with high-quality weather information to initiate a paradigm shift. This paper contributes by quantitatively investigating the effect of numerical weather prediction (NWP) accuracy on aircraft performance estimation. Nowcast/forecast of wind and temperature acquired from Local Forecast (LFM), Meso Scale (MSM), and Global Spectral (GSM) models were compared over a series of ADS-B data on three domestic flight routes. In the second phase, fast-time simulations were used to simulate several tactical and strategic air traffic management applications by calculating the flight time and fuel consumption over a cruise flight segment. Several flight levels corresponding to a year worth of weather data with feasible nowcast/forecast combinations were considered. The results showed that wind forecast deviations increase with the update cycle time and forecast horizon, and record up to 6 m/s compared with LFM nowcasts. Time thresholds were defined to investigate the impact on aircraft performance estimations, and the results revealed that LFM forecasts are feasible for applications with short-temporal forecast requirements. Also, just over 50% in total estimates were eligible for meeting the performance standards in terms of long-temporal strategies with MSM/GSM forecasts. Overall results elaborated the importance of a tailored approach for applying NWP data in future ATM operational strategies.

Tethered satellite formation systems have attracted significant attention in recent years, primarily because they offer potential advantages for certain space missions, such as space interferometry measurement. This work considers the stable deployment of a spinning multi-mass tethered system arranged in a hub-spoke configuration in the orbital plane. The system contains a parent satellite (hub) modeled as a rigid body, and several sub-satellites connected to the hub via inelastic tethers (spokes). The deployment dynamics are derived using Lagrange’s equations. The spinning motion of the parent satellite is controlled by active torque, while tether deployment is conducted by release mechanisms on the parent satellite and low-thrust engines installed on each sub-satellite. Considering the physical restraints of tether tension during the deployment process, an optimal controller is proposed using Bellman dynamic programming, based on a simplified dynamic model. Then, the obtained controller is employed in the complete model, where the coupling effect between the spinning of parent body and tether deployment are taken into account. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed control strategy.

This paper presents an adaptive controller using a neural network to compensate for model uncertainty during transition flight. Time-scale separated nonlinear dynamic inversion (NDI) control is applied to regulate tiltrotor UAV state against high nonlinearly. A neural network (NN) is also introduced to transition flight control to control the existing model uncertainties. Since the tiltrotor UAV can operate in both CTOL mode and VTOL mode, a flight control system according to each flight mode has been implemented. A linear control mixing technique is applied to the tilting angle for safe transition flight between two modes. The robust control method for the tiltrotor UAV proposed in this study was verified for altitude and attitude control in transitional flight numerical simulation.

Estimation of aircraft weight in flight, which is a dominant parameter related to its flight performance, was studied. A typical approach for the estimation is to use a simple combination of initial weight on the ground and fuel consumption in air obtained with fuel tank gauge or by accumulating fuel flow. This approach is insufficient when the flight performance will be estimated as accurately as possible because some of the available measurements are not utilized. Therefore, the forward–backward smoother derived from Kalman filter was applied to the estimation with our revision of the smoother to fuse terminal weight on the ground additionally. According to numerical simulations, our method estimated the weight in smaller errors than not only the typical approach but also a fixed-interval smoother, which is used generally for an off-line estimation problem. Moreover, application to actual flight data showed that our method improved the estimated standard deviation by approximately three percent at maximum compared to the fixed-interval smoother.

To operate an unmanned aerial vehicle (UAV) within a set of maneuverability constraints, herein, we use the Model Predictive Control (MPC) method to find the optimal control input under various control input constraints. Although the MPC method can predict future states and reflect them it the present state optimally, in real-world scenarios, its computational load increases exponentially with the number of state variables and the length of the time window. To reduce the computational burden and obtain the predicted optimal control input by using the MPC technique, herein, we devise an approach involving neural networks. To evaluate the weighting parameters of the neural networks, a considerable volume of learning data is required, which can be generated by conducting numerical simulations. Herein, we generate the input and output data pairs for a given time window by using the MPC method and by means of simulations. This learning process is expected to mitigate the computational burden dramatically. Lastly, one of the drawbacks of MPC is that the model for evaluating the optimal control input for a given time interval must be extremely accurate to guarantee system stability. Therefore, to increase the robustness of the MPC method against external disturbances and internal uncertainties, we augment it by using the Sliding Mode Control (SMC) method. The effectiveness of the suggested neural-network-based Model Predictive Sliding Mode Control method is demonstrated by means of numerical simulations.

To study the influence of rotation speed and rotor diameter on the control characteristics and flight performance of a tilt-rotor aircraft, a flight dynamics model and a required power calculation model of the tilt-rotor were established. Trim analysis was carried out under three typical flight modes, and the change of the required power was further analyzed. The results showed that as the rotor speed/diameter increased, the collective pitch and cyclic pitch of the rotor and the pitching angle of the fuselage decreased, and the deflection angle of the elevator increased. As the rotor speed increased, the required power of the tilt-rotor aircraft increased. In helicopter mode, the required power decreased with increasing rotor diameter, while in fixed-wing mode, the required power increased with increasing rotor diameter.

In this paper, we propose real-time optimal guidance for aircraft terrain-following. Terrain-following is an essential technique for military aircraft and should be carried out in a safe flight while maintaining the lowest altitude as possible. In terrain-following, it is important for aircraft to stay above the reference trajectory and fly well despite the presence of disturbances such as wind. In this work, we propose a model predictive control method that performs real-time optimization for terrain-following guidance. MPC reflects the model of the aircraft and several maneuver constraints, so optimal planning for terrain-following can be designed. The nonlinear kinematic model of the aircraft is defined, and successive convexification is performed to linearize the nonlinear model and successfully solve the optimization problem. The proposed algorithm was compared with the general guidance algorithm, in this work we compared it with the L1 navigation law. As a result of real-time simulations, the MPC guidance algorithm performed terrain-following with a safer and smaller error, even with the presence of disturbances.

The Unmanned Aerial Vehicle (UAV) swarm coordination control system is key to enabling UAVs to accomplish missions collaboratively without conflicts. A series of representative system architectures has been proposed following the top-down decomposition approach, dividing the challenging coordination control problem into several sub-problems. However, these explorations have usually been confined to model details and little has been done in the way of perception of the whole system. To create a unified understanding of the control system, a concept of a bio-inspired UAV swarm coordination control system is proposed in this paper, and the architecture of this system is described based on the domain meta-model (DMM) of the Unified Architecture Framework (UAF). Basic components of this conceptual system are extracted from predefined UAF domains, and customized meta-models of the bio-inspired UAV swarm coordination control system are established at each layer on the basis of general UAF DMM with selected standard UAF viewpoints. Introducing model-based system engineering methodology, the architecture description provides relevant developers with intuitive and coherent system perception. This architecture can also help generate a standard development procedure for the coordination control system and a reference for UAV swarm top-level design.

This paper presents an application of gain-scheduled (GS) control to a gust alleviation system based on the C* control law. The structure integrates a widely used flight maneuver and preview control systems using light detection and ranging. A set of state-space models derived at different flight Mach numbers is used to design the controller. In this study, the controller structure is selected as a linear function of the Mach number, i.e., the control gains are linearly interpolated with the current Mach number. We compare the GS and gain-fixed (GF) controller results at various airspeeds. The simulation result shows that utilizing the preview information improves the gust alleviation performance in GS and GF controls. In addition, the GS controller performs better than the GF controller in tracking the vertical acceleration command as the Mach number reduces.

This paper proposes a decentralized trajectory optimization method with conflict resolution for multiple unmanned aircraft systems (UASs) considering communication uncertainties from delayed position and velocity information. The method calculates the trajectory of each UAS while considering the possible reachable set of trajectories of neighboring UASs that result from the uncertainties of their positions and velocities. The overall problem is formulated as a model predictive control problem represented by a quadratically constrained quadratic programming problem that can be solved via successive convexification. The effectiveness of the proposed method is demonstrated by several numerical simulations.

For deep space landing missions, spacecraft are required to identify their expected landing sites autonomously because of the extremely long time delay caused by the distance between the spacecraft and Earth. This identification process is desirable to finish within several seconds by onboard computers with limited calculation performance. Moreover, autonomous identification based on natural features of landing sites are highly recommended in future missions, although some artificial target markers have been used for navigation and control to the landing site in some previous missions. To make fast but reliable identification of landing sites for the automatic task, this research utilizes a deep learning processing for images taken in different light-conditions and altitudes. First, a semantic segmentation model for rocks in terrain images is developed. For robust identification, some improvements are introduced in the semantic segmentation process. Then, to identify the same place in images taken at different altitudes, a comparison algorithm based on triangular shapes is applied. Thus after training, the semantic segmentation model can detect the same place from several images in a relatively short computational time.

Coaxial rotors are widely used for large-sized multi-rotors to achieve the efficiency of spatial layout. However, as the wake effect of the upper rotor affects to the lower rotor, performance degradation of the lower rotor is common. In this paper, a model of performance degradation for the coaxial rotor configuration is proposed based on experiment results on a given combination of lower and upper rotor pulse width modulation (PWM) signals. For the performance reduction model, loss of actuator effectiveness with respect to the given PWM signal of the lower motor is estimated comparing with the single rotor configuration. For the coaxial rotor experimental data, the PWM percent of the upper rotor is fixed with a constant value in each scenario, while the lower rotor angular velocity is changed with a specific interval in the coaxial rotor configuration. Also, the PWM percent of the upper rotor is changed with another specific interval for each of the scenarios. In each scenario, thrust, torque and angular velocity of the rotor are measured. For comparison, these same measurements were taken for the single rotor configuration with the same PWM percent interval of the upper rotor in the coaxially configured rotor. By comparing the coaxial rotor and single rotor configuration, thrust and torque efficiency degradation with respect to the PWM signal combination of the upper and lower rotors is derived. To evaluate the performance reduction of thrust and torque, the loss of actuator effectiveness with the function of the upper and lower rotor PWM signal is derived with the ratio between the single rotor and coaxial rotor configuration in the same PWM percent of the upper rotor. This can be applied to precise multi-rotor modeling for simulation or controller design.

Visual odometry (VO) has recently attracted significant attention, as evidenced by the increasing interest in the development of autonomous mobile robots and vehicles. Studies have traditionally focused on geometry-based VO algorithms. These algorithms exhibit robust results under a restrictive setup, such as static and well-textured scenes. However, they are not accurate in challenging environments, such as changing illumination and dynamic environments. In recent years, VO algorithms based on deep learning methods have been developed and studied to overcome these limitations. However, there remains a lack of literature that provides a thorough comparative analysis of state-of-the-art deep learning-based monocular VO algorithms in challenging environments. This paper presents a comparison of four state-of-the-art monocular VO algorithms based on deep learning (DeepVO, SfMLearner, SC-SfMLearner, and DF-VO) in environments with glass walls, illumination changes, and dynamic objects. These monocular VO algorithms are based on supervised, unsupervised, and self-supervised learning integrated with multiview geometry. Based on the results of the evaluation on a variety of datasets, we conclude that DF-VO is the most suitable algorithm for challenging real-world environments.

The recent emergence of advanced computing technologies has opened a world of possibilities for bridging virtual-physical spaces in the future of urban air mobility (UAM). Digital twin technology (DT) introduces a coupled virtual-physical asset system that evolves over time in their digital and physical state-spaces associated with the real-time exchange of observed data and control inputs for high-fidelity operational services in UAM, such as traffic management, or vehicle maintenance. On the other hand, edge computing has been envisioned as a dominant computing paradigm in future UAM infrastructures to enable infinitesimal-latency processing of massive and heterogeneous data acquired from ubiquitous devices and vehicles. Furthermore, federated learning (FL) has recently been proposed to resolve the performance and security problems of traditional AI techniques featured by centralized data collection and training to offer a decentralized and cooperative data training pattern among a huge number of devices/vehicles (aka., agents) in multi agent DT systems for UAM. Lastly, the blockchain has emerged as a ledger technology in which data and processes are divided into tiny data blocks and concatenated into a series to reinforce the security and scalability of decentralized computing patterns. Realizing the advances and integration of the emerging technologies mentioned above is of paramount importance for the development of DT systems in UAM. Previous studies have not completely considered the integration and harmony of these existing technologies for the development of UAM DT systems. There has also been a significant lack in comprehension and integration of emerging technologies for the development of UAM-DT systems. In that context, this study proposes a comprehensive integrated UAM-DT platform and solution of FL with the blockchain in edge computing for the development of UAM-DT systems, called BlockFE-DT. First, we introduce the adoption of a probabilistic graphical model as a formal mathematical foundation of coupled digital-physical twin systems for UAM, particularly for unmanned aircrafts. Afterwards, we review the fundamental concepts of FL, blockchain, and edge computing technologies to explore and propose an integration framework of FL with the blockchain in edge computing in the context of UAM-DT systems. Key issues in the integration are assimilated including explainable AI, dependability, and security. This work provides ground-breaking concepts and architectures for a blockchain empowered FL framework with edge computing in future UAM-DT systems.

In recent years, enhancing intelligence in the development of Unmanned Aerial Vehicles (UAVs) with a decrease in cost for the application of swarm fleets has attracted a variety of interest. Specifically, for urban applications such as public transportation, logistics mobilization, rescue operations, etc., there is a need for a fleet of UAVs to plan and coordinate intelligently without human intervention. A multi-agent reinforcement learning framework that can learn and make policies for such systems is desperately needed. This paper proposes an AI-based Bio-inspired Decentralized Multi-Agent Reinforcement Learning (B-DMARL) framework as a multi-agent actor-critic model for executing an assigned job in an increasingly dynamic environment. The B-DMARL is a distributed control architecture with two levels. For group coordination and collision avoidance, low-level control is developed using an AI-based bio-inspired steering behavior algorithm. Using a Proximal Policy Optimization (PPO) based reinforcement learning method, the agent is trained as a high-level control to correctly execute tasks in more dynamic environments. In a virtual simulation environment, the proposed B-DMARL framework is applied to persistent surveillance tasks that require the cooperation and collaboration of UAVs. Simulation results demonstrate that the proposed methods have an improved learning rate and reward signal.

With recent technological developments, The UAS (unmanned aerial system) has been recognized for its value and usefulness in various fields. Prior researchers have utilized several drones in collaboration to navigate to achieve common goals such as target tracking, rescue operations, and target-finding with multi-UAS systems. Multi-agent reinforcement learning algorithms are a type of artificial intelligence technology in which many agents collaborate to perform tasks. When a multi-UAS cooperative navigation technique is deployed to a complicated environment such as an urban logistics system, the agents’ learning capacities become more tedious. In this study, we present what is termed the improved Multi-Actor-Attention-Critic (iMAAC) approach, a modified multi-agent reinforcement learning method for application to urban air mobility logistic services. A virtual simulation environment based on Unity is created to validate the suggested method. In the virtual environment, the real-world situation of UAS logistics development services is replicated. When the findings are compared to those of other landmark reinforcement algorithms, iMAAC shows a higher learning rate than those by the other algorithms when utilized in multi-agent systems.

This paper focuses primarily on urban air mobility (UAM), including air transportation for passengers and goods in metropolitan areas. Simulations of UAM in urban environment are conducted. Firstly, quadrotor configuration is determined as the research object of this paper. Based on the flight dynamic equations, the 6-DOF mathematical model of quad-rotor UAS is established. A flight control law based on nonlinear dynamic inversion method is designed for the quadrotor to follow the expected path. Then, the airspace below 120 m is divided into several layers. UAM aircrafts with a similar flight heading fly in a same layer and the vertical separation of high risk UAM aircrafts can be maintained. The flight profile of UAM aircraft is designed based on the layered airspace, including the flight attitude and heading in the flight phases. Finally, to verify the concept proposed in this paper, several mathematic simulations are conducted based on the urban airspace environment described above. The simulations include complete flight processes of multi-UAM in layered airspace. The simulation data is collected and results are analyzed. It is concluded that with proper management of airspace and flight control algorithm, this work is able to simulate the UAM operation in urban environment. The proposed airspace management method could be effective to guarantee the flight safety of UAM. At last of this paper, the research conclusion and expected future work are discussed.

With the ever-increasing social labor cost and automation level, the research on the recognition and automatic classification and sorting of multi-target objects has important practical significance. To this end, a set of multi-target object recognition and sorting system based on Yolo v5 is designed, which is composed of three parts: image acquisition part, image target object recognition and positioning part, and sorting execution of different types of targets. First, the sorting system collects the original image of the target object by controlling the movement of the camera. Next, the Yolo v5 target detection algorithm is used to realize the identification of the target object, and at the same time return the position information of the target object to the control host. Finally, the upper host computer controls the movement of the mechanical claws to realize sorting by controlling the decision-making algorithm. In order to improve the recognition accuracy in the target detection process and compress the model size to adapt to the Jetson Nano embedded system, an improved Yolo v5 target detection algorithm is proposed. The experimental results show that the designed multi-target detection system can accurately identify, locate and classify target objects; at the same time, the improved Yolo v5 target detection algorithm proposed has a smaller model and faster recognition speed, which can be realized in Rapid deployment of embedded systems.

Guidance algorithms for the return flight of suborbital spaceplanes must generate a variety of guidance trajectories that satisfy terminal conditions even in unexpected abort operations. To tackle this issue, a trajectory optimization method that combines convex quadratic programming and a global derivative-free optimization technique in a nested structure has been recently studied by the authors. This hybrid method efficiently explores the three-dimensional Bezier trajectories and associated guidance commands that exactly fulfill the equality terminal conditions and command continuity. In this paper, Monte-Carlo simulations are performed to investigate the applicability of this guidance method to the realistic scenario of unpowered return flight. Six stochastic evolutionary algorithms, a Bayesian optimization method, and three deterministic search algorithms are implemented and tested as global optimizers. They are compared in terms of computational and implementational complexities, robustness, and diversity of solutions obtained. The results show that reliable and real-time trajectory generation is possible, when an optimizer and its settings are properly chosen. It also reveals that diverse trajectories between initial and terminal conditions are successfully generated.

A multi-sensor fusion approach to high-precision entry phase navigation for Entry, Descent, and Landing (EDL) operation is presented. A single Ultra High Frequency (UHF) band radar, two ranging beacons, an on-board Inertial Measurement Unit (IMU), pressure measuring devices on the heat shield of the entry vehicle, and heat flux measuring devices on the spacecraft’s back shell are combined to give highly accurate estimates of states and parameters. Full kinematic states, lift to drag ratio, and nominal atmospheric density are estimated. The Unscented Kalman Filter (UKF) is used for fusion as it yields good performance for systems with high degrees of non-linearity. Smaller position estimation errors enable the exploration of areas of high scientific value. Accurate lift to drag ratio estimates have important implications for achieving control goals during entry. A good estimate of nominal atmospheric density provides an accurate model of the variation of atmospheric density with altitude. This is critical for the design of soft-landing entry vehicles and for environmental study. The utilization of a wider set of sensing modalities than is reported in any literature so far makes this work unique. Our findings are substantiated with MATLAB® simulations.

UAVs (Unmanned Aerial Vehicles) and drones have been used in various fields such as delivery of goods, fire suppression, and traffic monitoring. As a result, the number of beginners in UAV control is also increasing. Traditional methods of UAV control include manual flight using twin-stick controllers or automatic flight by uploading Waypoint-based missions to the FC (Flight Controller) in advance using GCS (Ground Control System) software. However, these conventional UAV control methods take a long time for beginners to get used to it. In manual flight, considerable proficiency is required for UAV control, which increases the control fatigue of novice operators. For beginners, it may be difficult not only to control UAV, but also to handle other tasks such as manipulating a gimbal-camera or starting/pausing missions at the same time as UAV operations. Especially in emergency situations, simultaneous work can be very burdensome because the ability to respond quickly is required. Therefore, it is necessary to develop an operator-friendly control system with HMI (Human Machine Interface) (Zimmer D, Rhodes D (2006) Human–machine interfaces. IEEE Indus Appl Mag 12:29–35) that reduces the operator's controlling stress and does not require high-level control capabilities. We have developed the UAV control system with speech recognition, which is used for AI speakers that are easily seen around us, and gesture recognition, which is used for motion recognition cameras like KINECT, based on the existing control systems. We have implemented speech recognition techniques using PyAudio and SpeechRecognition, speech recognition open-source libraries, and gesture recognition techniques using YOLOv3, an object-detecting open-source library. First of all, to select speech and gesture commands, we analyze the UAV operation procedure and divided step by step. Second, the commands are adopted according to the divided UAV operation steps. Finally, we develop modules to process these commands and do the SITL (Simulation In The Loop). In this paper, we have designed a prototype of an Operator-friendly Control System with HMI (Human Machine Interface) capable of simple speech and gesture commands, and proceed with the SITL. Based on our UAV Control System with HMI, we can identify which sensors and which commands are Operator-friendly. The entire process is simulated using ROS Melodic and gazebo9 in Ubuntu 18.04 environments. As a result, we expect that the people who have no experience in operating UAVs can operate the UAV with HMI control system using speech and gesture recognition in the simulation.

The small Unmanned Aircraft Systems (sUAS) with Beyond Visual Line of Sight (BVLOS) have already been used for commercial operation in urban, which triggered the new operation risk. At present, the Specific Operation Risk Assessment (SORA) method has widely adopt by the competent authorization, but the acceptable means of compliance (AMC) are not sufficient to support the implementation of SORA. Overall, the determination of operational volume (OV) is one of the most urgent problems. In this paper, we firstly supposed the OV geography and summarized the possible operation scenarios. Then the determination method was proposed based on the key influencing factors. Finally, an urban logistical operation case with large amount of practical data was selected to verify the method. The results were analyzed and demonstrated the method was available.

In this study, the flight quality of the transition process for a classic type of tilt-rotor vertical take-off and landing unmanned aerial vehicle (VTOL UAV) was improved by optimizing the transition process of this type of UAV. The optimization process is as follows: (i) the nonlinear dynamic models of aircraft were established. (ii) the tilting path of the tilt-rotor aircraft was designed by the optimal method. In order to minimize the attitude change, control energy consumption, and transition time of the aircraft during the transition process, a novel optimization cost function was built as objective for transition time, pitch angle change, and the control surface deflection minimization. The tilting strategy of the transition process was proposed based on the Gaussian pseudo spectral method. (iii) A weighted pseudo-inverse method based on niche genetic algorithm was used to design the manipulation strategy with the goal of the highest control efficiency and the smoothest transition, solving the problem of control redundancy caused by mode conversion and the lack of yaw control capability during the early stage of conversion. The simulation results show that compared to the traditional transition strategy, the novel tilting trajectory designed in this study brings better transition qualities, and the new control allocation law increases the aircraft yaw control capability, which in turn enhances the stability and anti-interference ability of the UAV in the transition mode.

Trimmable horizontal stabilizer (THS) is an important surface for flight controls. The actuator driving THS is designed to ensure high reliability and fault tolerance. With the development of more and more electric aircraft, THS has changed from traditional hydraulic drive to electric drive or hybrid drive, and the control method has also changed. By analyzing the system architecture of EHTSA, this paper discussed the transfer function of the system, and derived the control parameters of current loop and speed loop. The overall system model is established in AMESim simulation platform. Through simulation analysis and comparison of control strategies: When the position open-loop and speed closed-loop control mode is adopted, the position of the surface can be controlled by judging whether the command is completed through position feedback, but it is not accurate enough. Using position closed-loop control, although the position response speed increases, the stability decreases.

Excessive maneuvering may pose risk of damage to the aircraft. This paper presents methods to solve this problem. Specifically, the shortest path between two given points in the 3D space of an unmanned aerial vehicle (UAV) is generated, and the generated path is traced. Moreover, the designed controller is introduced. The path generation algorithm is based on the existing path generation algorithm pertaining to the Dubins curve. The path generated by this algorithm can satisfy the constraints regarding the minimum allowable turning radius; moreover, the algorithm involves rapid computation and can be applied for path generation in real time. Notably, in this algorithm, the shortest path to fixed initial and final points is geometrically obtained in a 2D plane; however, real aircraft path generation problems must be implemented in 3D. Therefore, a path in 3D space is generated by deriving a solution through a numerical technique, using the same principle as that of the problem in 2D. Furthermore, the line-of-sight guidance algorithm corresponds to basic waypoint guidance. The proposed 3D path tracking approach is implemented using a nonlinear controller, resembling those of line-of-sight guidance algorithms. The controller guarantees, under certain assumptions, asymptotic tracking of the path, in terms of both the position and attitude. Finally, we adopt a sliding mode controller, which is a robust controller that can ensure high control performance and stability even under uncertainties. The proposed controller satisfies the constraint through the saturation function, specifically, by introducing the constraint on the angular velocity to the existing sliding mode controller. The stability of the designed controller is verified considering Lyapunov stability, and the performance of the proposed algorithm is verified through numerical simulation.

An algorithm is proposed for generating phased array radar terrain scan data from a simulated radar. To obtain simulated radar terrain scan data, it is assumed that the radar beam is scanned sequentially within certain azimuth and elevation areas. From this assumption, the radar terrain scan data are generated by integrating the detected information from each radar beam. The algorithm for simulating terrain scan data of the radar largely comprises two parts. One part of the algorithm generates terrain scan information from individual beams. The other integrates the scan information of individual beams into entire scan data. Although the proposed algorithm has some limitations, it provides a reasonable result that reflects the characteristics of an actual radar. Furthermore, the proposed algorithm is expected to have an impact on terrain following (TF), which needs accurate terrain scan data to generate a precise terrain profile.

This paper is described to give the initial information for representing the analysis of preliminary design of the multiple jointed combined cycle (MJCC) engine which is combined with three different engines, turbine engine, dual mode scramjet (DMSJ) and rocket. In starting a practical design of a combined cycle engine, it is very complex and interactive between sub-engines, so a preliminary design is required and can give the requirement for each sub engine comprising the integrated propulsion such as the program MJCC. Through this paper, how to derive preliminary design and procedure along the pre-defined mission profile are described. Furthermore, the analytical results of the preliminary design are included in this paper. MJCC engine is composed of the representative two combined cycle engines, a combined cycle turbo-ramjet engine, and a combined cycle dual mode scramjet engine with rocket combustor installed in it operating independently and interactively at the different Mach (velocity) range, lower, super and hyper speed. The combined turbo-ramjet engine operates at the range of Mach 0–4 and the rocket combined DMSJ operates Mach 4–8 higher and the MJCC engine also gets thrust only by rocket without incoming air from the intake at the higher Mach 8+ with additional specific oxidizer. For preliminary design and analysis of this engine, first, the mission profile and aerodynamic forces were derived from the result of other research. Next, the required thrusts of sub-engines were obtained with initial stage engine models constructed for this objective considering design and off-design point of each engine. From that, the initial relative size of engine components such as intake, combustor, nozzle is calculated and the effective combination of two engines is suggested considering this relative size. In conclusion, this result certainly can give the guideline for the preliminary design of a combined cycle engine and the initial parameter for the practical design.

This research examined the concept of a heavy rocket equipped with air-breathing ejector rocket engines as a means of transporting large quantities of supplies into space. The design was based on the Japanese LE-7A engine of the H-2A rocket. These ejector rocket engines can provide large thrust augmentation by re-heating unburned hydrogen with atmospheric oxygen sucked in by an ejector effect. Using a simple analysis of the heavy rocket flight performance, it was found that the air-breathing effect of the ejector rocket engine appeared up to an altitude of 17.5 km. It was also found that a 26% propellant reduction and a 53% payload increase were possible.

Nowadays the civil aircraft engines are exploiting larger fan diameters and higher bypass ratios to reduce specific fuel consumption (SFC) and CO2 emissions, providing stringent environment guidelines. This paper aims to study the effects of the HPC interstage bleed, power off-take and IPC bleed air fraction on the transient performance of a three-shaft turbofan engine for entry into service (EIS) 2050. The influences of the bleeding air fraction with 1, 3 and 5% on the critical components, net thrust and SFC were investigated. Afterwards, the interstage bleed was set at the HPC inlet, 2nd, 3rd, 4th stage of the HPC to seek the optimum bleed strategy. Then power extraction was designated as 50 kW, 100 kW, 150 kW respectively to study the transient performance variation. Moreover, the corresponding temperature overshooting exerted on the turbine blade life was predicted. Finally, an 8100 nm of flight mission evaluation in terms of block fuel, NOx, CO2 and H2O was conducted to explore the above factor impact. The findings showed that increasing IPC bleeding air boosts the Fan and IPC surge margin. The interstage bleed position and IPC bleed air fraction were influential in mission performance. The HPC interstage bleed position at the 4th stage would lead to around 1.70%, 1.95%, 1.45% and 1.65% increase in block fuel, NOx, CO2 and H2O compared to the 2nd stage. As the IPC bleed rises from 1 to 5%, a 1.65% extra fuel would be consumed. The NOx and CO2 emissions go up by 1.0% and 1.61% respectively.

In order to tackle the global warming problems caused by carbon dioxide and respond to the urgent need for energy saving and emission reduction, the ecological footprint in the space field should be drastically reduced to finally realize the zero-carbon emission flight. Considering the characteristics of high calorific value, wide flammability and zero carbon emissions, hydrogen shows great potential advantages in powering aircraft. This paper develops a hypersonic turboramjet model using hydrogen to replace traditional kerosene. The design target is to realize the hypersonic flight with Mach number 6.0 and high altitude ranging from 25 to 40 km. Based on the over-under turboramjet configuration, a robust boundary approaching design method was proposed and implemented. The technology level in 2060 was predicted and exploited to minimize the specific fuel consumption (SFC). To achieve the lightweight engine architecture, the light-density and temperature-resistant ceramic matrix composite (CMC) turbine and micromix combustor were employed. The corresponding cycle parameters, net thrust, fuel consumption and engine weight were obtained. The findings showed that the optimum switch point for the turbojet and ramjet was at Mach number 3.0 and altitude 25 km where the turbojet pressure ratio was 18. The feasibility check of the turboramjet proved good reliability with the aerodynamic loading and strength capability within the limitations. The utilization of hydrogen could dramatically lower SFC by more than 60% relative to the kerosene turboramjet. This engine features a lightweight, clean and economical aerospace power plant, which can efficiently complete the flight envelope mission, prolong the flight range and broaden the combat radius.

Solid scramjet can be divided into solid rocket scramjet and solid fuel scramjet. Compared with solid fuel scramjet, the solid rocket scramjet has the advantages of good flame stability and more alternative mixing methods. Boron is widely used in solid fuels as a solid additive with high calorific value. However, the hard to volatilize oxide layer prolongs the ignition time of boron particles. In addition, the short residence time of particles in the supersonic afterburning chamber makes it difficult for the energy of primary gas with boron contained to play a full role, which makes the performance of solid rocket scramjet insufficient.In order to improve the combustion efficiency of primary gas with boron particles contained in the combustion chamber of solid rocket scramjet for enhancing the engine performance, the effects of different blunt body types and blunt cone angle on the combustion characteristics are calculated and analyzed by numerical method. The results show that the combustion efficiency of boron particles in the second chamber with basic blunt body is the highest, but the specific impulse is the lowest among the three types of different bluff bodies. For the influence of bluff body cone angle, the combustion efficiency of bluff body combustor with 30 degree angle is the highest among 25, 30 and 35 degree angles. But when the cone angle is 25°, the specific impulse is the highest.

Performance deterioration of gas turbine engines in service is inevitable. Several severe damages could be generated, such as fouling, erosion, corrosion, rubbing wear, hot end component damage. All these cumulative effects would cause deterioration and the engine might not satisfy the design requirements. This paper aims to investigate the influences of component degradation regarding efficiency and flow capacity on the engine performance, transient behaviour and aircraft level performance. 1%, 3% and 5% degradation of each component is studied separately to obtain the resultant engine deterioration. Considering the engine deterioration is always caused by the combination of efficiency and flow capacity, several cases with superimposed factors are simulated in the flight mission analysis. The outcomes indicate that LPT and HPC efficiency degradation exerts an obvious effect on engine performance. A 5% decrease in LPT and HPC efficiency would result in a 4.74% increase in SFC and a 4.27% rise in T4 separately at the takeoff point. More importantly, the operating lines of Fan and IPC are pushed towards the surge line during a slam operation to take-off condition. The combination of efficiency and mass flow degradation of 5% for each component demonstrates a noticeable 5.8% rise in block fuel. Furthermore, the corresponding NOx and CO2 are unavoidably increased by 15.3%, 5.8% and 5.8% respectively. It could be concluded that component degradation should be closely observed to prevent catastrophic consequences.

In recent years, the emission regulations of civil aircraft have become stringent, which requires a significant reduction in CO2, NOx and noise emissions by 2050. One important aspect is to figure out the influences of critical parameters on the engine performance and aircraft mission assessment. This paper carries out sensitivity analysis regarding the T4, component efficiency and pressure ratio, aiming at achieving the most influential factors for engine design. The increment for each parameter was scheduled as 1, 2 and 3% separately to uncover the dominant factors. Furthermore, the effects of a 3% increase in the parameters individually were studied in both the steady-state and mission level performance. It is found that Fan efficiency and LPT efficiency were the most important factors that impact engine steady-state performance. The 3% increase in the two factors would result in 1.75 and 1.49% reduction in SFC whereas 2.12 and 1.52% boost in net thrust. This finding has highlighted the importance of improving the efficiency of the low-pressure system. After reaching the optimum T4 limit, the thermal efficiency is reluctant to ascend for the fixed OPR case. Furthermore, the betterment in component efficiency is preferred and lead to a noticeable improvement in reducing block fuel and emitted emissions. The corresponding fuel saving, NOx and CO2 emission prevention were 2.30, 4.06 and 2.29% respectively when the Fan efficiency is levelled up by 3%. Although a lower combustor pressure loss would definitely save fuel and generate less CO2, it would produce additional NOx compared to the baseline engine.

The Turbine–fan air cycle refrigeration systems based on the reverse Bretton cycle have been widely used since the 1940s, whose cooling process is accomplished by an air cycle machine (a combined fan, and turbine machine), temperature and flow control valves, and heat exchangers using outside ambient air to dispense waste heat. It is characterized by fewer system accessories and the ability to refrigerate on the ground, but its refrigeration efficiency and dehumidification efficiency are low. So there are often free water in the cabin, which is an important factor that restricts the use of the system and the passenger comfort. The turbine–fan high pressure water separation refrigeration system based on energy recovery technology is proposed for the first time in this paper, which not only takes into account the characteristics of the traditional Turbine–fan air cycle refrigeration system, but also can use high pressure water separation technology to reduce the moisture content in the system, which fills in the blank of the development history of the air cycle refrigeration systems.

To verify the effectiveness of a novel axisymmetric scramjet, Improved Delay Detached Eddy Simulation (IDDES) coupled with Dynamic Zone Flamelet Model (DZFM) based on 60 million cells was conducted to investigate the performance of the full-scale engine. Four mesh sets with different refinement levels and adopting hexahedral structured cells were used for the grid independence verification. The pressure agrees well with the experimental data. Due to the absence of the corner effect, the boundary layer is thinner and the jet penetration depth is low. The poor mixing between the transverse fuel jet and the crossflow causes weak combustion and a low-pressure rise ratio. The cavity plays four roles in enhancing combustion: a radial pool, a low-speed bay, a high-temperature zone, and a premixer. Axisymmetric scramjets without any flame holders overall have poor mixing and combustion performance.

This paper presents a brief introduction of the gas turbine combined heat and power system. Taking full use of the rejected heat generated by the gas turbine, the efficiency of the combined systems is significantly improved as well as saving energy and cost. Unlike the simple cycle engine, the fundamental systems consisting of a gas turbine system, heat recovery steam generating system, cooling system and steam system, which makes the whole system complex and complicated. The performance, especially the part-load performance and ambient environment influences were investigated in order to understand the working characteristics of the combined system. Meanwhile, as the combined systems play a great role in mitigating pollution, the mechanism of emissions generation and curbing methods were described. The combined systems are of various usage and could easily adjust according to the requirements, which makes them prosperous in the industry. Finally, the promising future of the combined systems is discussed and concluded.

Exemption is a way for aeroengines to obtain airworthiness type certification, which can exempt aviation products from obligation to comply with a certain regulation directly on the premise of proving equivalent level of safety. The regulation requirements for exemption home and abroad were introduced in this paper. The exemption cases of aeroengines recorded in the FAA exemption system in recent decades were analyzed. Through an in-depth analysis of documents approved by the authorities, five main types of applications for exemption were summarized as inherited, optimized, deviated, unverifiable and restricted. The frequency and difficulty of all kinds of exemption types are analyzed. Finally, an in-depth discussion on the application of exemption was made, including the application object of various exemption methods, the relationship among them and the priority of application. This study is the first time in China to conduct a full-process and multi-level analysis of airworthiness exemption methods, which fills the gap in domestic aeroengine airworthiness exemption research and provides a reference for domestic self-developed aeroengines to apply for airworthiness exemption.

This paper aims at the investigation of the vibration mechanism and dynamic response of the power gearbox using the established gear transmission system model. A comprehensive scheme was built consisting of modeling, assembling, boundary settings and loading to assess the deformation and vibration characteristics of the gearbox. Meanwhile, the transmission errors were included in the model to consider the effects of system errors, surface modification and manufacturing errors. Afterwards, the three –dimensional (3D) model was launched in Romax software to study the static and vibratory characteristics of the gearbox system. In addition, the meshing stiffness and transmission errors were analyzed. Finally, the gear surface modification and optimization were carried out to achieve the optimal stress distribution and reduce the maximum contact stress. The simulation results show that the maximum first mode acceleration response within the operating range was 8 g by setting the sensor at the planet wheel shaft pin. Meanwhile, the estimated transmission errors ranged from 0.13 ~ 0.15 μm. The maximum load for the planet roller bearing was 2933 N and would generate 1219 MPa contact stress, which was reasonable and acceptable. Nevertheless, the contact patch was unevenly distributed under the high-load conditions. To be specific, the maximum stress was 419 MPa locating at the gear surface edge, posing a failure risk. By performing the surface modification, the maximum stress has significantly dropped to 202 MPa and the corresponding region has moved from the edge to the surface center. The results has highlighted the importance of the proposed GTF gearbox system dynamic simulation scheme and generate a applicable approach for the transmission system engineering design.

The clearance has an imperative influence on the efficiency and safety of the aero-engine, and the clearance should be obtained by measuring in a working engine for the design and control of the clearance. Foreign aero-engine companies have applied high-energy X-ray technology to engine measurement to analyze the motion of working components. At present, there is no precedent for the application of this field in China, its specific measurement and verification methods were not mastered, and there was no ability to use high-energy X-rays to measure the clearance of the engine. In this paper, the measuring principle of high energy X-ray is analyzed, and the test method of turbofan engine clearance measurement based on high energy X-ray is investigated according to the structural characteristics of a turbofan engine. Besides, the test scheme of the clearance measurement and the calibration method of test data are proposed, and the turbofan engine clearance measurement based on high energy X-ray is achieved for the first time in China. This breaks through the bottleneck of engine rotor and stator axial absolute deformation and the thermal clearance measurement after working, contributing to the improvement of engine clearance design ability in China. Depending on the comprehensive analysis of engine measurement requirements, equipment acquisition ability, data reading boundary, test procedure, absolute coordinates, and engine temperature, an effective test scheme is developed for the first time. The valid data of the deformation and clearance in the axial and radial directions between rotors and stators (labyrinth seals and blade tip) are achieved. Three kinds of comprehensive calibration methods are innovatively established, namely, the clearance measurement of the capacitive sensor, the differential measurement of the wear deformation of labyrinth seals, and the measurement of axial deformation of rotor axial force. Additionally, the correction and verification of high-energy X-ray measurement data are completed by those data. Finally, the motion law of key components in the working and the change law of key clearance after working of a certain engine are obtained. This result can provide a useful reference for the popularization and application of high-energy X-ray technology in the field of engine clearance measurement technology in China.

This paper addresses the development and application of transient mathematical models and the in-house simulation code for the KRE-75 engine, which will be used as the 1st and 2nd stage engines of the Korea Space Launch Vehicle II. KRE-75 is the first Korean open-cycle liquid rocket engine that uses kerosene and liquid oxygen as propellants. Most of the components comprising the KRE-75 and the priming process of propellants in the propellant feed lines are considered. Startup and shutdown simulations of the KRE-75 are compared with engine ground test results and discussed in detail. The simulation results show good agreement with the test results, proving the validity of the transient mathematical models and the simulation code.

Pintle injector is well-known for its high performance in overall thrust level of throttling. This feature is attributed to its ability to control propellant injection area, in which one propellant is injected radially and the other propellant is injected to annular direction. However, despite to this capability to regulate injection area, the momentum of radial flow in low throttling level is usually not sufficient to obtain appropriate spray characteristics, which include spray angle and droplet size. As a solution of this problem, a grooved pintle tip is proposed in this work. This grooved tip has several grooves on the upper side of pintle tip where radial flow is formed, and it blocks the injection area at different ratio depending on the throttling level. Cold flow tests using normal and grooved pintle tip with changing mass flow rate and annular orifice area were conducted, and the spray characteristics of pintle injector with these two types of pintle tips were compared. When grooved pintle tip was applied, spray angle of pintle injector was larger than that of the normal pintle tip case. Also, the droplet size was smaller than when a normal pintle tip was used. Therefore, the grooved pintle tip may contribute to preventing spray characteristics of pintle injector from deteriorating combustion characteristics in low throttling level, while maintaining the spray characteristics as in the case of normal pintle tip at middle and high throttling levels.

An electric pump-fed cycle pressurizes the propellant using a pump, in a manner similar to how a gas generator cycle works. Because a pump is electrically driven by a motor, an electric pump-fed cycle has a relatively small number of components and a simple system compared to a gas generator cycle. Therefore, an electric pump-fed cycle has advantages in that the development costs and required skill levels are lower than those associated with a gas generator cycle. In addition, due to the improved technology of electric motors and batteries, it is considered that an electric pump-fed cycle can be efficiently operated in a commercial space launch vehicle. With regard to the battery technology prospects, the energy density of a battery will be improved by four times by 2050, which will increase the payload capacity of a launch vehicle with an electric pump-fed cycle. Therefore, studies of electric pump-fed cycles are underway in many countries. Most of the studies involve a theoretical performance analysis for a comparison with other feed systems or are limited to design studies of specific components of the electric pump-fed cycle. In order to develop integrated feed system technology for an electric pump-fed cycle, experimental work is done using test equipment. In this study, the 30 kN class engine of an electric pump-fed cycle with LOx/LCH4 is scaled down to 500 N using lab-scale test equipment. A Run tank, feed line, motor pump assembly, and valves were designed as components of the electric pump-fed cycle. The initial pressurization and flow control method were also analyzed and designed. The oxidizer and fuel volume flow rates are determined to 5.79 and 4.53 lpm, respectively. In each pump, the pressurization performance requires pressure that exceeds 2.5 MPa. In addition, flow rate control by the opening of a valve can be controlled more broadly than the RPM controlling approximately 4.7%.

The scramjet combustor model installed with a micro-burner torch and cavity was tested in a combustion wind tunnel facility at the Kakuda Space Center of the Japan Aerospace Exploration Agency (JAXA). A methane-ethylene mixture gas fuel (36%/64%) was injected directly into the cavity. The aim of this dissertation was to clarify the forced ignition characteristics of the hydrocarbon mixture fuel in scramjet combustors. First, the effect of the fuel supply rate on the forced ignition limits was experimentally evaluated followed by an evaluation of the effect of the torch gas temperature on the forced ignition limits. In this study, the OH* chemiluminescence emission was measured to determine the ignition in the cavity. The torch gas entered directly into the shear layer between the main and recirculation flows within the cavity. When the methane-ethylene mixture fuel reached the leading edge of the cavity where air was plentiful, the mixture fuel was ignited in the shear layer. The results indicated that it was necessary to evaluate the ignition within the recirculation zone as well as in the shear layer.

Growing interest and demand for efficient, cost-effective propulsion for small spacecraft platforms have driven the endeavor devoted to downscaling electric propulsion systems. Cusped field thrusters (CFTs) are advantageous over other electrostatic types such as gridded ion engines and Hall Effect thrusters, featuring enhanced electron confinement enabled by magnetic mirror using permanent magnets hence longer lifetime expectation. Physical modeling and characterization of performance are essential for design optimization of CFTs, but rather few research efforts have been dedicated to them to date. A multi-objective design optimization study is performed in the present study, based on evolutionary algorithms incorporating magnetic simulation coupled with an improved power balance model. It aims to simultaneously maximize the performance parameters, namely thrust, total efficiency, and specific impulse, with the anode voltage and current, mass flow rate, and magnet radii employed as the decision variables. Covariance-based global sensitivity analysis is conducted to identify influential design parameters. Uncertainty analysis is performed using prediction from surrogate models via machine learning by means of Monte Carlo simulation to examine the effects on uncertainties in the design parameters on the performance parameters. The plasma behavior inside the channel and the plume region has been investigated with primary focus on the magnetic field strength. In so doing, physical insights have been gained into key design factors to maximize CFT performance.

Supercritical combustion occurs in the high-pressure environment of a rocket engine where supercritical atomization is required. In this experiment, a kerosene surrogate, the primary fuel of a liquid rocket, is selected and injected into a high-temperature, high-pressure chamber above the critical point. The experiment is conducted to analyze the jet penetration sprayed from the subcritical state to the supercritical environment and to confirm the phase change at the jet-ambient gas interface. The atomized jet is generated by impinging a hydrocarbon mixture propellant, and this experiment examines the phase change in a supercritical environment. A mixture of n-decane and methylcyclohexane is used as the surrogate propellant. The propellant is injected into the chamber of the sub/supercritical environment, and the jet core and penetration are visualized using a shadowgraph method. The ambient pressure exhibits two conditions: 2.6 MPa ( $${P}_{r}$$ = 1.00) and 3.6 MPa ( $${P}_{r}$$ = 1.38). These conditions have different pseudo-critical points, and the experiment is carried out while increasing the ambient temperature from 599 K ( $${T}_{r}$$ = 1.00) to 700 K ( $${T}_{r}$$ = 1.17). At 2.6 MPa, the density of the propellant decreases drastically near the critical temperature of the mixture, 602 K. At 3.6 MPa, the density decreases near 630 K, a pseudo-critical temperature. Consequently, it is confirmed through the visualization that the length of the liquidlike core of the jet is shorter at 2.6 MPa than at 3.6 MPa, all else being equal.