Fault-tolerant Flight Control and Guidance Systems

This book deals with the design of fault-tolerant control and guidance systems for a small unmanned aircraft. This book focuses on designing techniques to detect and isolate faults among sensors and actuators and on developing methods to appropriately reconfigure the flight control laws and the vehicle trajectory. Real-time capability and modularity are two main requirements for the algorithms designed.

This chapter reviews some of the most relevant fault-tolerant flight control systems that can be found in the literature. Since the terminology used in this field is not unique and differs among authors, the chapter starts with a brief definition of some terms and expressions frequently used throughout this book.

This chapter presents the axes, the frames, and the nonlinear model of the aircraft used in this book [1–4].

In this chapter, three main limitations of the classical implementation of the MMAE method to isolate faults based on predefined fault hypotheses are highlighted. The first limitation concerns the number of filters that must be designed in order to span the range of possible fault scenarios, which must be limited due to computational load. The second limitation appears when an actuator is locked at an arbitrary non-zero position that biases the residuals of the KFs, leading to inaccurate fault detection and state estimation. Third, most of the implementations of an MMAE method only work efficiently around predefined operating conditions. This chapter presents a nonlinear actuator FDI system, which works over the entire operating envelope of an aircraft. Locked-in-place and floating actuator faults can be handled. The robustness of the FDI system is enhanced by the use of auxiliary excitation signals. The FDI system is also capable of handling two simultaneous actuator failures with no increase of the computational load. The complete system has been demonstrated in simulation with a nonlinear model of a model aircraft in moderate to severe wind conditions.

This chapter describes the design of a control allocation module with explicit laws for fast operation and low computational load, such that this algorithm can run in a small processor or microcontroller with limited floating-point operation capability. The control allocation method is capable of compensating for actuator faults. Given the appropriate fault detection system, there is no need to redesign the controller when such faults occur, since the control allocator compensates for the fault. The allocation method is also designed to be reconfigurable based on the results obtained from the EMMAE-FDI system presented in Chap. 4. Finally, this chapter terminates with a comparison, which shows that this method yields satisfactory results, provides optimal solutions in some cases, and is simpler and faster than conventional methods.

An aircraft is intrinsically a nonlinear system. Therefore, if linear controllers are to be used in the aircraft flight control system, several linear controllers have to be designed and then gain-scheduled over the operating regime of the aircraft. However, recent nonlinear control techniques have made it possible to deal directly with the known nonlinearities of the aircraft dynamics, which yields a unique controller suitable for a wide range of operating conditions. This chapter describes the technique known as NDI and presents the architecture and the design procedure of the controllers used in the aircraft autopilot.

The nonlinear differential equations governing the motion of an aircraft are described in Chap. 3. For the plant analysis and control design, these equations are linearized around a certain operating point. Two sets of state variables appear to be clearly decoupled, each defining a specific mode of aircraft motion. The state variables involved in the longitudinal mode are the pitch rate q, the airspeed V _T , the angle of attack α, and the pitch angle θ. The lateral-directional mode involves the state variables for the roll rate p, the yaw rate r, the sideslip angle β, and the roll angle φ. This chapter is dedicated to the analysis and control of the longitudinal motion of the aircraft and presents an architecture for the altitude controller, which uses robust NDI in all of the control loops. This chapter brings an innovative and practical approach for stability and robustness analyses of the plant undergoing the dynamic inversion process. Moreover, this chapter provides a systematic procedure for the selection of some uncertain model parameters involved in the controllers. Finally, a new nonlinear airspeed controller is also designed and presented.

This chapter is dedicated to the analysis and control of the lateral motion of the aircraft and presents an architecture for the lateral-directional controllers which use robust NDI in all of the control loops. This chapter brings an innovative and practical approach for stability and robustness analyses of the plant undergoing the dynamic inversion process. Finally, this chapter provides practical suggestions for the selection of the uncertain model parameters involved in the controllers.

This chapter presents a guidance algorithm for a UAV. It combines a nonlinear lateral guidance control law, originally designed for UAVs tracking circles for mid-air rendezvous, with a new simple adaptive path-planning algorithm. Preflight path planning consists only of storing a few waypoints guiding the aircraft to its targets. The chapter presents an efficient way to model no-fly zones (NFZ), to generate a path in real time to avoid known or “pop-up” obstacles, and to reconfigure the flight path in the event of reduced aircraft performance. Simulation results show the good performance of this reconfigurable guidance system which, moreover, is computationally efficient [1, 2].

After an actuator failure, the performance of the aircraft is degraded. If an FDI system is available in the flight control system, the knowledge of the failure can be used to evaluate the new aircraft performance. Based thereon, a supervision system decides whether the mission can still be continued or if it should be aborted and have the aircraft redirected to the base station. In both cases, the aircraft should still be guided along a trajectory that is compatible with the new flying properties of the airplane. This chapter focuses on an aileron failure and shows how the degraded flying performance can be evaluated and used to reconfigure the guidance system. Simulation results show that, if the reduced performance due to the actuator failure is taken into account, the safety of the mission is improved [1].