Abstract
AbstractThe issue of robust actuator fault reconstruction for a class of Takagi‐Sugeno (T‐S) fuzzy systems with actuator fault and unknown input via a novel Synthesized Learning and Sliding‐Mode Observer (SLSMO) is investigated in this paper. Through a coordinate transformation technique, the considered T‐S fuzzy system is decomposed into two separate subsystems: Subsystem 1 affected only by actuator fault and Subsystem 2 affected by unknown input and actuator fault. In the SLSMO methodology, for Subsystem 1, a new reduced‐order Fuzzy Learning Observer (FLO) is explored to accurately reconstruct actuator fault, while a reduced‐order Fuzzy Sliding Mode Observer (FSMO) is employed for Subsystem 2 such that it has strong robustness against actuator fault and unknown input. Stability and convergence of the fuzzy SLSMO are explicitly proved using Lyapunov's indirect method. The design issue of the reduced‐order FLO and of the reduced‐order FSMO can be uniformly formulated in terms of some Linear Matrix Inequalities (LMIs) that can be directly solved using LMI optimization technique. In addition, a new full‐order FLO is suggested for actuator fault reconstruction in a class of T‐S fuzzy system without unknown input. At the end, a numerical example is applied to verify the effectiveness and superiority of the proposed approaches.
Published Version
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