Actuator Fault Reconstruction for Takagi-Sugeno Delay Systems via a Robust Fuzzy Learning Observer

Abstract

This paper addresses the problem of robust actuator fault reconstruction for Takagi-Sugeno (T-S) delay systems subject to actuator faults and unknown inputs via a Robust Fuzzy Learning Observer (RFLO). Based on $H_{\infty}$ control methodology and learning algorithm, a full-order RFLO is constructed for T-S fuzzy systems with time-varying delays such that it can not only estimate system states, but also achieve robust reconstruction of actuator faults. The stability and convergence of the proposed RFLO is proved using Lyapunov stability theory and $H_{\infty}$ control technique. The design of the proposed RFLO can be formulated in terms of a series of Linear Matrix Inequalities (LMIs) that can be conveniently solved using LMI optimization technique. Finally, a truck trailer system is employed to verify the effectiveness of the proposed fault-reconstructing strategy.