Adaptive neural fault-tolerant control for uncertain MIMO nonlinear systems with actuator faults and coupled interconnections

Abstract

Handling both intermittent actuator faults and coupled interconnections in uncertain multiple-input–multiple-output nonlinear system is still a challenge in the control community. In this paper, to address this issue, an adaptive neural fault-tolerant control scheme is developed. Firstly, neural networks with random hidden nodes are used to approximate unknown functions, and an inequality is introduced to construct controllers such that the singularity problem of the controllers can be circumvented. Secondly, a projection algorithm is adopted to update online the estimated parameters in the controllers such that the boundedness of estimated parameters is ensured. In particular, the boundedness of estimate of unknown fault parameters with intermittent jumps can be definitely guaranteed. Due to the effects of intermittency jumps of unknown parameters on the system stability during operation, a modified Lyapunov function is developed to prove the system stability. It is proved that the system stability only depends on the jumping amplitude of Lyapunov function and the minimum fault time interval and is not affected by the total number of faults. Thirdly, a root mean square type of bound for the tracking error is established by using iterative calculation to illustrate that the system transient performance in the sense of the tracking error is adjustable by proper choice of design parameters. Finally, simulation studies are carried out to verify the effectiveness of the theoretical results.