Fuzzy model predictive control using Takagi-Sugeno model

In this paper we present the nonlinear model predictive control based on the Takagi-Sugeno fuzzy model. The paper is divided into two parts. The first part focuses on the fuzzy model-identification, in which we employ the Takagi-Sugeno fuzzy model - a powerful structure for representing nonlinear dynamic systems. The second part emphasizes on the objective function optimization by using the branch and bound method (B&B) and genetic algorithm (GAs). Two methods were used as constrained optimizers to online plan optimal input policies over a defined prediction horizon basing on the identified fuzzy model. To reduce computational effort, we combine B&B with dynamic grid size method and GAs with fuzzy adaptive interval. Both developed methods are programmed and tested to control the liquid level of two tanks system which has hard nonlinearity and long delay time. Simulation results show that the proposed methods are successfully applied to nonlinear systems. Some comparisons about ldquooptimardquo solutions and time executions are discussed.