Hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems

In this paper, we develop a hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems. Takagi-Sug eno (TS) fuzzy model is used to model the chaotic dynamic system and the ex tended parallel-distributed compensation technique is proposed and formulat ed for designing the fuzzy model-based controller under stability condition s. The optimal regional-pole assignment technique is also adopted in the de sign of the local feedback controllers for the multiple TS linear state-spa ce models. The proposed design procedure is as follows: an equivalent fast- rate discrete-time state-space model of the continuous-time system is first constructed by using fuzzy inference systems. To obtain the continuous-tim e optimal state-feedback gains, the constructed discrete-time fuzzy system is then converted into a continuous-time system. The developed optimal cont inuous-time control law is finally converted into an equivalent slow-rate d igital control law using the proposed intelligent digital redesign method. The main contribution of this paper is the development of a systematic and effective framework for fuzzy model-based controller design with dual-rate sampling for digital control of complex such as chaotic systems. The effect iveness and the feasibility of the proposed controller design method is dem onstrated through numerical simulations on the chaotic Chua circuit.