LSTM-Based Output-Constrained Adaptive Fault-Tolerant Control for Fixed-Wing UAV with High Dynamic Disturbances and Actuator Faults

• Published in: Mathematical problems in engineering Vol. 2021; pp. 1 - 18
• Main Authors: Chang, Xiaofei, Rong, Lulu, Chen, Kang, Fu, Wenxing
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Actuators; Adaptive control; Approximation; Attitude control; Constraints; Controllers; Design; Disturbances; Fault diagnosis; Fault tolerance; Faults; Fixed wings; Mathematical problems; Methods; Neural networks; Nonlinearity; Unmanned aerial vehicles
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2021/8882312

The unknown disturbances and the changing uncertainties bring difficulties for designing a stable attitude controller for UAV. In this paper, a novel adaptive fault-tolerant attitude control approach is designed based on the long short-term memory (LSTM) network for the fixed-wing UAV subject to the high dynamic disturbances and actuator faults. Firstly, the high dynamic disturbances can be compensated by the adaptive laws. Meanwhile, the actuator faults can be handled by the proposed adaptive fault-tolerant control (AFTC) scheme. Moreover, the LSTM network is introduced to approximate the unknown and time-accumulating nonlinearities. With the introduction of the one-to-one nonlinear mapping (NM), the output constraints in the control system can be guaranteed. Additionally, it can be demonstrated that the boundness of all the signals can be assured. At last, numerical simulation results are provided to illustrate the effectiveness of the proposed method.