Adaptive Fuzzy Model Predictive Control for non-minimum phase and uncertain dynamical nonlinear systems

this paper introduces a method to design a robust adaptive predictive control based on Fuzzy model. The plant to be used as predictive model is simulated by TakagiSugeno Fuzzy Model, and the optimization problem is solved by a Genetic Algorithms or Branch and Bound. The method to tune parameters of the model predictive controller based on Lyapunov stability theorem is presented in this paper to bring higher control performance and guaranty Global Asymptotical Stable (GAS) for the closedloop system. This method is used for nonlinear systems with non-minimum phase (CSTR), uncertain dynamical systems and nonlinear DC motor. The simulation results for the Continuous Stirrer Tank Reactor (CSTR), nonlinear uncertain dynamical system and nolinear DC motor are used for verifying the proposal method. Index Terms Model Predictive Control (MPC); Takagi Sugeno Fuzzy Model (TS); Genetic Algorithms (GAs); Branch and Bound (B&B), Multiple Inputs-Multiple Output (MIMO); Single Input-Single Output (SISO); Adaptive Fuzzy Model Predictive Control (AFMPC); Global Asymptotical Stable (GAS); Continuous Stirrer Tank Reactor (CSTR).