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A new strategy for fault estimation in Takagi-Sugeno fuzzy systems via a fuzzy learning observer

This paper is to suggest a new strategy for fault estimation in Takagi-Sugeno (T-S) fuzzy systems. A fuzzy Learning Observer (FLO) is constructed to achieve simultaneous estimation of system states and actuator faults. The FLO is able to estimate both constant and time-varying faults accurately, and a systematic method is also proposed to select gain matrices for the FLOs. Stability and convergence of the proposed observer is proved using Lyapunov stability theory. The design of FLOs can be formulated in terms of Linear Matrix Inequalities (LMIs) that can be conveniently solved using LMI optimization technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-estimating approaches.