Neural Network-Based Adaptive Fault-Tolerant Control for a Class of High-Order Strict-Feedback Nonlinear Systems

The adaptive fault tolerant control (FTC) problem is investigated for a class of high-order strict-feedback nonlinear systems with the actuator faults, and an adaptive fault tolerant control strategy is proposed in this paper. Compared with the traditional first-order strict-feedback nonlinear systems, high-order strict-feedback nonlinear systems are more general, but more difficult to handle. In particular, this system occurs actuator failure, which generates the additional terms. To address the unknown nonlinearities in the system, radial basis function neural networks are introduced to approximate the unknown continuous nonlinear functions. Based on Lyapunov stability theory, it is proved that the tracking error converges to a small adjustable neighborhood of the origin with all signals in the closed-loop system being bounded. Finally, a numerical example is used to verify the effectiveness of the control scheme proposed in this paper.