Output feedback fault-tolerant control for a class of nonlinear systems via dynamic gain and neural network

• Published in: Neural computing & applications 2020-05, Vol.32 (10), p.5517-5530
• Main Authors: Xi, Xiaoye, Liu, Tingzhang, Zhao, Jianfei, Yan, Limin
• Format: Article
• Language: English
• Subjects: Actuators; Adaptive control; Artificial Intelligence; Atci 2019; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Control systems design; Coordinate transformations; Corresponding states; Data Mining and Knowledge Discovery; Fault tolerance; Feedback control; Image Processing and Computer Vision; Neural networks; Nonlinear systems; Output feedback; Parameter uncertainty; Probability and Statistics in Computer Science; State observers; State variable; Unmanned aerial vehicles
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04583-1

In this paper, by combining the dynamic gain and the self-adaptive neural network, an output feedback fault-tolerant control method was proposed for a class of nonlinear uncertain systems with actuator faults. First, the dynamic gain was introduced and the coordinate transformation of the state variables of the system was performed to design the corresponding state observers. Then, the observer-based output feedback controller was designed through the back-stepping method. The output feedback control method based on the dynamic gain can solve the adaptive fault-tolerant control problem when there are simple nonlinear functions with uncertain parameters in the system. For the more complex uncertain nonlinear functions in the system, in this paper, a single hidden layer neural network was used for compensation and the fault-tolerant control was realized by combining the dynamic gain. Finally, the height and posture control system of the unmanned aerial vehicle with actuator faults was taken as an example to verify the effectiveness of the proposed method.