Design of sampled-data fuzzy-model-based control systems by using intelligent digital redesign

In this brief, we develop an intelligent digital redesign method for hybrid state space fuzzy-model-based controllers, effective for stabilization of continuous-time uncertain nonlinear systems under discrete-time controller. Takagi-Sugeno (TS) fuzzy model is used to represent the complex system as multiple and uncertain linear state-space models over different local operating regions. A continuous-time fuzzy-model-based controller is then synthesized for stabilization, where the guaranteed-cost design method is utilized to cope with system uncertainties. The local controllers of the continuous-time fuzzy-model-based controller are then converted to equivalent discrete-time counterparts and aggregated through the fuzzy inference system to give a discrete-time fuzzy-model-based controller. Finally, a TS fuzzy model for the chaotic Lorenz system is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.