This paper is concerned with the problem of robust fault estimation and fault-tolerant control for a class of Takagi-Sugeno (T-S) fuzzy systems with time-varying state delay and actuator faults. Based on the (k − 1)th fault estimation information, a novel k-step fault-estimation observer is proposed to construct the kth fault error dynamics. The obtained fault estimates via k-step fault-estimation can practically better depict the size and shape of the faults. Then, based on the information of online k-step fault-estimation, a dynamic output feedback fault tolerant controller is designed to compensate the fault effects on the closed-loop fuzzy system. Furthermore, some less conservative delay dependent sufficient conditions for the existence of fault estimation observers and fault tolerant controllers are given in terms of solution to a set of linear matrix inequalities. Finally, simulation results of two numerical examples are presented to show the effectiveness and merits of the proposed methods. Index Terms-Dynamic output feedback control, fault tolerant control (FTC), k-step-fault-estimation, linear matrix inequalities (LMIs), Takagi-Sugeno (T-S) fuzzy systems, time-varying delay.

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SummaryThe fault estimation for a class of nonlinear systems with Lipschitzian nonlinearities and faults is studied in this article. An integrated estimation observer that covers the robust estimation observer (REO) and adaptive estimation observer (AEO) is proposed to improve the accuracy of fault estimation. Compared with the traditional AEO, the designed observer does not involve the output derivatives and can be more suitable for practical applications. Furthermore, based on the designed observer, the coupling term emerging in the obtained error dynamics can be eliminated reasonably and less conservative stability conditions for the error dynamics can be obtained, whereas the case is hard to be achieved based on the existing intermediate estimator approach in the literature. Compared with the traditional REO and AEO, the fault can be estimated with a good accuracy by using the proposed integrated estimation observer. Numerical examples test the effectiveness and advantages of the proposed method.

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