ELM-Based Adaptive Fault Tolerant Control of Uncertain Nonlinear Systems with Actuator Faults

Abstract

Actuator faults have been thought to be one of the most main challenges to be addressed because the system stability can be seriously deteriorated by incorrect actuator actions. Therefore, in this paper, an adaptive neural fault tolerant control (FTC) method is developed for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems which are subject to actuator faults and external disturbances. The proposed method utilizes extreme learning machine (ELM) neural networks which are of random hidden nodes to approximate the uncertain modules of systems due to their universal approximation property. Meanwhile, our method does not require prior knowledge of the considered systems outlined, and can effectively compensate for the effects of the actuator faults and external disturbances. Moreover, it is proved that all signals in the closed-loop system remain uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the proposed FTC method.