Disturbance Observer-based Adaptive Fault-tolerant Dynamic Surface Control of Nonlinear System with Asymmetric Input Saturation

Abstract

In this paper, a composite fault-tolerant control problem is studied for a class of uncertain nonlinear system with asymmetric input constraint, actuator fault and external unmatched disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown uncertainty and asymmetric input saturation. The approximation error, external unmatched disturbance and actuator faults are integrated as the compounded disturbance. A nonlinear disturbance observer is designed to tackle the effect of the compounded disturbance which can be separated from the controller design. To handle the effect of asymmetric input saturation, a smooth continuous differentiable saturation model is explored. Adaptive NN fault-tolerant control scheme is developed to guarantee that all the signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) and the tracking errors converge to a small neighborhood of origin by choosing the appropriate design parameters. The effectiveness of the proposed control scheme is demonstrated in the simulation study.