Double layered nonlinear model predictive control method based on the T-S fuzzy model

Abstract

In this paper, double-layered nonlinear model predictive control method based on T-S fuzzy model is proposed. Firstly, the nonlinear system will be described by the T-S fuzzy model. The method takes advantage of fuzzy clustering algorithm to identify premise parameters of the T-S fuzzy model and applies to the weighted recursive least squares method with forgetting factor for identifying consequent parameters of the T-S fuzzy model. Based on the T-S fuzzy model, double-layered model predictive control with the “steady state target calculation + dynamic control” can be designed. Finally, by solving the optimization problem with constraints, the state feedback control gain matrix can be derived. CSTR process simulation results show that the nonlinear systems can be very accurately described by the T-S fuzzy model. And the system can be controlled very stablely by the proposed control algorithm, which has strong robustness and practicality.