Tuning function-based adaptive backstepping fault-tolerant control for nonlinear systems with actuator faults and multiple disturbances

Abstract

The aim of this paper is to deal with the tracking control problem of strict-feedback nonlinear systems subject to unknown actuator faults and multiple disturbances simultaneously. To tackle the problem, a novel adaptive backstepping fault-tolerant control algorithm which is based on the tanh function, the tuning function, and neural networks is proposed. During the procedure of controller design, the effects of unknown actuator faults modeled as loss of effectiveness are eliminated without a fault detection and isolation mechanism. The multiple disturbances composed of the matched disturbance and the unmatched disturbance are approximated by neural networks and compensated by the tuning function-based adaptive technique, respectively. Moreover, the over-parametrization phenomenon resulting from unknown parameters is avoided via the tuning function. The sufficient condition for the closed-loop system to be asymptotically stable is gained through constructing an appropriate Lyapunov function. The presented scheme is reduced to two numerical examples, in which the practical system for high-speed trains is considered. Simulation results prove that the chattering phenomenon is eliminated via tanh function and the presented fault-tolerant strategy is feasible and effective.