Adaptive predictive control based on fuzzy model and genetic algorithms for uncertainty nonlinear system.

Abstract

The paper presents a method to design the Adaptive Predictive Controller for uncertainty nonlinear system. The predictive model  is used by  a group of Takagi-Sugeno Fuzzy Models with Fuzzy Switching Element, the Optimisation Problem is solved by the Genetic Algorithms. The method to tuning the parameters of the Model Predictive Controller based on Lyapunov stability theorem is presented in this paper. These tuning parameters are the weight coefficients of the costfunction. These coefficients are tunned to bring higher control qualities and guaranty Global Stable System (GAS) for the closed system. Simulation results show that the proposed controller can be  applied for uncertainty nonlinear plant. The paper is organised as follows: The description  of MPC based on Fuzzy Model and Genetic Algirithms is section 1, Takagi-Sugeno Fuzzy Model and Genetic Algirithms are presented in section 2, The method to tuning the parameters of the Model Predictive Controller based on Lyapunov stability theorem is  section 3, The simulink  and results are presented in section 4, Conclutinons are in section 5.