State Estimation of Stochastic T-S Fuzzy Systems

Since the T-S fuzzy system can approximate any nonlinear system with arbitrary accuracy, it is also expected to be a suitable approach to observe the states of a stochastic nonlinear system. Up to date, a few state estimators for stochastic T-S fuzzy systems have been proposed and applied to various fields without rigorous proof. In this paper, we first derive a sufficient condition based on the linear matrix inequality theory for the stability of general state estimators. Furthermore, under Gaussian noise assumption, we derive the optimal Fuzzy Kalman Filter for stochastic T-S fuzzy systems. It is shown that from the perspective of conditional estimation, state estimation of a stochastic T-S fuzzy system is actually a linear estimation problem.

並列關鍵字

State Estimation ； Stochastic Fuzzy System ； Fuzzy Kalman Filter

被引用紀錄

Chen, C. K. (2008). 以元件為組成基礎之分散式系統的計算與通訊最佳化 [doctoral dissertation, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2008.00603

Liu, H. J. (2011). 以肌電波為基礎之機器手臂運動控制 [doctoral dissertation, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2011.00053

鄭育旻（2006）。嵌入式多處理器FPGA系統之軟硬體分割技術〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2006.00218

Lin, Y. C. (2007). 具分散式及非正規設計之超長指令集數位訊號處理器架構之編譯器設計與最佳化研究 [doctoral dissertation, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-1411200715075731

Huang, C. C. (2007). 支援數位訊號處理器之微核心設計與雙核心開發環境 [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-1411200715100492

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State Estimation of Stochastic T-S Fuzzy Systems

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