Actuator fault detection and estimation for a class of nonlinear systems

Abstract

In this paper, a novel actuator fault detection and estimation scheme based on adaptive observer is investigated for a class of nonlinear systems. In this study, actuator faults are modeled by radial basis function (RBF) neural network. The adaptive fault estimation observer is designed by exploiting the online learning ability of radial basis function neural network to approximate the actuator fault. The weight updating algorithm of the RBF network is established in the sense of Lyapunov theory. In addition, design of the proposed observer is reformulated to a set of linear matrix inequalities, which can be easily solved by numerical tools. Finally, the presented fault detection and estimation scheme is applied to a satellite attitude control system. Simulation results demonstrate the effectiveness of the proposed fault diagnosis approach.