Neural network-based adaptive output feedback fault-tolerant control for nonlinear systems with prescribed performance

Abstract

In this paper, an adaptive fault-tolerant control (FTC) approach is proposed for nonlinear systems involving input saturation and unknown control directions. In the framework of dynamic output feedback FTC, neural networks (NNs) are used to approximate the unknown nonlinear functions, and then a novel adaptive controller is further designed by combining the Nussbaum function property. Unlike some existing works incorporating an auxiliary state variable, the asymmetric actuator saturation is handled by the Nussbaum function in this approach. Furthermore, it is proven that all signals in the closed-loop system are bounded and the output tracking error satisfies the prescribed performance condition through using the barrier Lyapunov function method. Finally, simulation examples are presented to illustrate the effectiveness of the proposed control method.