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基于模糊学习观测器的一类具有时变时滞的Takagi-Sugeno模糊系统的鲁棒故障估计
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Abstract:
本文研究一类具有连续状态时变时滞、执行器故障和范数有界外部干扰的Takagi-Sugeno(T-S)模糊系统的鲁棒故障估计问题。利用H∞优化技术，构造了一种新颖的模糊学习观测器，实现了系统状态和执行器故障的同时估计。基于Lyapunov稳定性分析方法，以一组线性矩阵不等式(LMIs)的解形式给出了误差动态系统的稳定性分析和一个保守性更小的时滞相关的充分条件。最后，通过一个动态模型的仿真结果说明了所提方法的有效性。
This paper concerns the problem of robust fault estimation for a class of Takagi-Sugeno (T-S) fuzzy systems subject to continuous state time-varying delay, actuator faults and norm-bound external disturbances. A novel fuzzy learning observer is constructed to achieve simultaneous estimation of system states and actuator faults by using theH∞optimization technique. Based on the Lyapunov method, stability analysis for the error dynamic and one less conservative delay dependent sufficient conditions are formulated in terms of solutions in a set of Liner Matrix Inequalities (LMIs). Finally, simulation results of a dynamic model are illustrated to show the effectiveness of the proposed approaches.
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