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Robust Guaranteed Cost Control for Uncertain Stochastic Fuzzy Systems with Aperiodic Sampled-data Based on Hybrid Modeling

Shuqi Li, Feiqi Deng*, and Jing Xiao
International Journal of Control, Automation, and Systems, vol. 20, no. 5, pp.1439-1448, 2022

Abstract : This paper aims at exploring the effective technique to analyze and synthesize the discrete feedback problem. It is particularly concerned with the robust guaranteed cost sampled fuzzy control problem of uncertain stochastic T-S fuzzy systems. Firstly, we propose the improved hybrid modeling technique to cope with sampled control input so that the premise mismatching problem of the stochastic fuzzy systems and fuzzy controllers can be well solved. Then, we provide the improved time-varying Lyapunov function method to analyze the remodeled special stochastic fuzzy impulsive problems which the spectral radius of impulsive gains are not less than 1. With this method, the control synthesis problem can be addressed rather than given the control gains in former hybrid modeling results. Finally, a numerical experiment is used to verify the effectiveness of our results. From the simulation results, we see that the hybrid modeling technique can get larger upper bound of the sampling intervals than results in existing modeling technique.

Keyword : "Aperiodic sampled-data, hybrid modeling, impulsive, stochastic fuzzy systems, time-varying. "

 
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