Actuator fault tolerant control for a class of nonlinear systems using neural networks

In this work, an actuator fault tolerant control scheme is developed for a class of uncertain single-input and single-output (SISO) nonlinear systems with the actuator fault. To develop the actuator fault tolerant control scheme, the radial basis function neural network (RBFNN) is employed to approximate the unknown system uncertainties as an universal approximator. Based on bakstepping method, the actuator fault tolerant control scheme is proposed for uncertain nonlinear systems via utilizing the output of the RBFNN. The closed-loop stability is rigorously proved under the developed adaptive fault tolerant control scheme via Lyapunov analysis and the ultimately bounded convergence of all closed-loop signals is guaranteed. Simulation results are given to show the effectiveness of the proposed actuator fault tolerant control scheme.