Affine TS-model-based fuzzy regulating/servo control design
Fuzzy Sets and Systems
Probabilistic fuzzy logic system: a tool to process stochastic and imprecise information
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A probabilistic fuzzy logic system: learning in the stochastic environment with incomplete dynamics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Adaptive fault-tolerant tracking control of near-space vehicle using takagi-sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
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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.