Control of Autonomous Aerial Vehicles

Although unmanned aerial vehicles (UAV) are now commonly employed for tasks that do not require interactions with the environment and other vehicles, such as collecting images and videos, these vehicles are not yet employed in more complex tasks as it would have been envisioned a decade ago. The reasons for this state-of-the-art are numerous, and mostly to be sought in the need for advanced guidance, navigation, and control systems that enable autonomy in complex, dynamic environments. In this chapter, we briefly outline some key aspects of the state of UAV technology. Successively, by providing the outline of this book, we explain in what directions research has been steered to address key shortcomings of UAVs for civilian applications.

This chapter addresses an online optimization on the path following guidance law for an unmanned aerial vehicle (UAV) against an external disturbance. The guidance problem is formulated in the nonlinear model predictive control (NMPC) framework with a nonlinear discrete-time kinematics model. The derived error dynamics with respect to the flight path are solved by numerical optimization minimizing the associated performance index in conjunction with equality/inequality constraints and the terminal condition. An NMPC solves the online optimal control problem at each sampling time by repeating the prediction of the trajectory of a nonlinear system over a receding time horizon from the current time and the generation of the control input sequences by solving the given optimal control problem. Numerical simulation demonstrates the effectiveness of the NMPC-based guidance algorithm compared with other benchmark guidance schemes.

This chapter presents an original guidance system for autonomous multi-rotor unmanned aerial vehicles (UAVs) equipped with forward-facing cameras and tasked with creating maps of unknown environments while operating in a tactical manner and at very low altitudes. The few existing guidance systems for UAVs operating in potentially hazardous environments essentially assume direct information on the location and the kind of potential threat to the aircraft, do not account for the UAV’s dynamics, and usually assumes that the UAV operates at high altitudes. The proposed guidance system, on the contrary, assumes no prior information on the environment and does not rely on external sources of information. Furthermore, to enable operations at low altitudes and in cluttered environments, the proposed guidance system includes a fast trajectory planner. For these features, UAV employing this guidance system can be employed by first responders and other emergency units to collect real-time data about a given location. Several unique features distinguish the proposed guidance system, including an original algorithm to cover connected set, which allows users to prioritize accuracy over flight time, an original algorithm to produce convex constraint sets in real time from voxel maps, and original approaches to induce tactical behaviors both in the optimization-based path planner and the model predictive control-based trajectory planner underlying the proposed guidance system. Numerical simulations validate the applicability and the effectiveness of the proposed guidance system.

Differential dynamic programming (DDP) is a well-recognized method for computational guidance due to its fast convergence characteristics. However, the original DDP requires a predefined final time and cannot handle nonlinear constraints in optimization. This prohibits the application of DDP to autonomous vehicles due to the heuristic nature of setting a final time beforehand and the existence of inherent physical limits. This chapter revisits DDP by dynamically optimizing the final time via the first-order optimality condition of the value function and using the augmented Lagrangian method to tackle nonlinear constraints. The resultant algorithm is termed flexible final time-constrained differential dynamic programming (FFT-CDDP). Extensive numerical simulations for a three-dimensional guidance problem are used to demonstrate the working of FFT-CDDP. The results indicate that the proposed FFT-CDDP provides much higher computational efficiency and stronger robustness against the initial solution guess, compared with the commercial-off-the-shelf GPOPS toolbox.

Unmanned air vehicles and urban air mobility have driven the need for the development of new algorithms for flight management systems. However, for certification, there is a strong desire to compare the new algorithms with the ones used for more conventional aircraft such as turbofan fixed-wing vehicles. With this need in mind, the main contribution of this chapter is to propose a unified approach for the maximum endurance of turbojet, turboprop, turbofan, and all-electric aircraft. This chapter details the two main steps to achieve this goal. First, a unified model of energy-depletion dynamics encompassing both fuel-burning and all-electric aircraft is developed. Next, using the unified energy model, the problem of maximum cruise endurance is formulated in a unified framework for all aircraft configurations and for a flight with three phases: climb, cruise, and descent. The problem is formulated and solved using hybrid optimal control in conjunction with the unified energy model. The general solution is then applied to turbojet, turbofan, turboprop, and all-electric aircraft. Simulation results are provided for a turbofan and a turboprop aircraft. Conclusions compare the results and summarize the most important findings.

Estimation of a source term, including the origin and release rate, for reconstructing a hazardous chemical, biological, or radiological substance dispersion event in the atmosphere is very important for public safety. The increase in the potential danger of hazardous substances leakage accidents and the threat of malicious acts in random places makes the estimation of the source term difficult using traditional systems such as pre-installed ground sensors in specific areas or ground vehicles. Unmanned aerial vehicles (UAVs) can be considered as an alternative solution for estimating the source term because they can be deployed to any arbitrary place and rapidly cover relatively larger areas compared with ground-based systems. This chapter introduces autonomous source search and estimation strategies for UAVs. Bayesian inference-based estimation approaches that can accurately estimate the source term in turbulent and noisy environments are presented using domain knowledge such as the plume dispersion and sensor models. In particular, since the estimation problem is highly nonlinear and non-Gaussian, the sequential Monte Carlo method (i.e., particle filter) Besides, various information-theoretic decision-making strategies are introduced using different information measures to determine the most informative sampling point at each time step using different information measures. To use the interaction and information sharing among multiple agents at best, cooperation and sensor fusion strategies are also discussed. Finally, comprehensive numerical simulations and flight experiments are presented to validate and compare the performance of the proposed strategies.

Unmanned aerial vehicles (UAVs) suffer from intolerable sensor drifts in global positioning system (GPS)-denied environments, leading to potentially dangerous situations. This chapter proposes a safety-constrained control framework that adapts UAVs at a path re-planning level to support resilient state estimation in GPS-denied environments. The proposed framework consists of an anomaly detector, a resilient state estimator, a robust controller, a pursuer location tracker (PLT), and an escape controller (EsC). The detector ensures anomaly detection and provides a switching criterion between the robust control and emergency control modes. PLT is developed to track the pursuer’s location by the unscented Kalman filter with sliding window outputs. Using the estimates from PLT, we design an EsC based on the model predictive controller such that the UAV escapes from the effective range of the spoofing device within the escape time that is defined as the safe time within which the estimation errors remain in a tolerable region with high probability. Subsequently, the proposed framework is extended for the multi-UAV systems that perform the time-critical coordination task.

The interest in agile maneuvering unmanned aerial vehicles (UAVs) specifically the quadrotor has increased considerably. The control of UAVs at high speed becomes a challenging task due to unmodeled aerodynamic forces and moments. In this study, position and attitude tracking controllers are presented in a structured cascaded fashion using incremental nonlinear dynamic inversion (INDI). A new approach for yaw rotational dynamic INDI control law is introduced, which simplifies the nonlinear dynamic allocation equation and actuator state feedback calculation by eliminating rotor acceleration and motor time constant terms. The closed-loop stability of the rotational and translational INDI controller is analyzed in detail. The significant improvement over the legacy proportional-integral-derivative (PID) controller is shown in outdoor flight tests. Circle and lemniscate-shaped trajectories are tracked with a maximum speed of 15 m/s. The attitude hold and tracking performance are evaluated with a maximum speed of 30 m/s.

Incremental control was proposed to reduce the model dependency of classical nonlinear control algorithms. Despite classical nonlinear controllers such as backstepping (BKS) controllers, incremental control systems, such as incremental backstepping (IBKS) controllers, utilize state derivatives and control input measurements instead of model information. In this chapter, we present the design of an incremental controller, and its closed-loop characteristics are provided. Remarkably, in the absence of uncertainties in the model and the measurements, the closed-loop transfer functions with IBKS and BKS are shown to be the same. To display the features of this controller, IBKS is applied to design the controller for a 6-degrees-of-freedom (DoF) unmanned aerial vehicle (UAV). Then, its performance is compared to that of a BKS controller to show that the model dependency is reduced by utilizing the state derivative and control input measurements. A closed-loop analysis with IBKS is conducted for three different cases. In the first test case, which involves model uncertainties and ideal measurements, unlike BKS, the closed-loop stability and performance of IBKS are not affected if the control system is fast enough. In the second test case, wherein both model uncertainties and measurements are biased, a steady-state error is experienced, but the system’s stability is unaltered. In the third test case, which involves model uncertainties and measurement delays, the closed-loop system’s stability is guaranteed by IBKS only if the delays on the state derivatives and the control input measurements satisfy a certain relationship which is dependent on the model uncertainty in the control effectiveness information.

Adaptive dynamic programming (ADP) is a sub-field of approximate dynamic programming that deals with the adaptive control of continuous nonlinear dynamic systems. Its origins stem from dynamic programming in optimal control, but it is extended into a form where approximations are used to reduce the curse of dimensionality and reduce the need for model knowledge. ADP is also considered to be one of the main reinforcement learning (RL) approaches since it uses information obtained from interaction with the environment to improve its policy. RL in general and ADP in particular are well suited for application to autonomous aerospace systems, since they allow adaptive control in case of uncertainties or faults in the system, even if the fault is of a type that is not anticipated during the control design. This chapter first gives a brief historical overview of ADP applications to flight control tasks. After that, four recent advances of ADP for flight control are presented.

We propose a distributed adaptive control approach to the cooperative output regulation problem for a class of heterogeneous uncertain multi-agent systems over general directed graphs. This approach breaks the original problem into two problems: (i) cooperative linear output regulation with an internal model-based distributed control; (ii) model reference adaptive control for each agent. Specifically, a distributed reference model characterizing the desired closed-loop response is constructed by solving the first problem. We show that the output of this reference model is tracked by the uncertain multi-agent system while keeping all the state variables of the closed-loop system bounded by solving the second problem. The decoupling between these two problems is achieved by a new decoupling virtual tracking error (that is, information exchange between agents). A numerical example demonstrates an application of the proposed approach to a heterogeneous multi-vehicle system consisting of unmanned aerial and ground vehicles in formation.

This chapter investigates the problem of an aerial manipulator interacting with the environment. The chapter is split into two parts. The former considers an aerial device with tilting propellers that, thanks to a super-twisting slide mode controller, can control the interaction force for inspection task purposes. The latter proposes a hardware-in-the-loop simulator for human cooperation and environmental interaction with an aerial manipulator. This part includes the mathematical background and theoretical derivation with insights into the relative stability proofs. Simulations in a highly realistic environment endowed with a physics engine and real experiments validate both the proposed approaches.

This book presented in detail how some key aspects in the design of guidance, navigation, and control systems for unmanned aerial vehicles (UAVs) have been addressed in recent years by experts in the field. This final chapter draws some conclusions on the state-of-the-art and discusses future research directions that embrace not only UAV technology but also futuristic applications such as urban air mobility and advanced air mobility.