00000cab a22000003a 4500 UP-99796217609532518 Buklod 20231007234347.0 m |o d | ta 101104s xx d | ||r ||||| || DENGII eng Bor-Sen Chen Optimal tracking design for stochastic fuzzy systems. pp. 796-813 In general, fuzzy control design for stochastic nonlinear systems is still difficult since the fuzzy bases are stochastic so as to increase the difficulty and complexity of the fuzzy tracking control design. In this study, a fuzzy stochastic moving-average model with control input (fuzzy ARMAX model) is introduced to describe nonlinear stochastic systems. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is developed. Based on a fuzzy one-step ahead prediction stochastic model, optimal design algorithms are proposed to achieve the optimal tracking of nonlinear stochastic systems. In this study, the minimum variance tracking control, generalized minimum variance tracking control, and the optimal model reference tracking control are developed for stochastic fuzzy systems. We construct some basic stability conditions for general stochastic fuzzy systems and use these conditions to verify the stability of the fuzzy tracking control systems. Finally, two simulation examples are given to indicate the performance of the proposed methods. Fuzzy ARMAX model. Fuzzy one-step ahead prediction model. Fuzzy stochastic moving-average model. Fuzzy tracking control design. Generalized minimum variance tracking control. Nonlinear system. Optimal design algorithms. Optimal model reference tracking control. Optimal tracking. Optimal tracking design. Stability conditions. Stochastic fuzzy systems. IEEE Transactions on fuzzy systems 11, 6 (2003). FO UPD DENG-II Article