Nonlinear Model Based Predictive Control using Fuzzy Models and Genetic Algorithms

Abstract

Abstract : Nonlinear model base predictive control (MBPC) is one of the most powerful techniques in process control, however, two main problems need to be considered : obtaining a suitable nonlinear model and an efficient optimization procedure. In this paper, fuzzy Takagi-Sugeno (TS) models are used for nonlinear systems modeling and the optimization routine is based on genetic algorithms(GAs). First a fuzzy TS model of the non-linear system is derived from input-output data by means of fuzzy clustering and least squares parameter estimation. Next, the fuzzy model is used in an MBPC structure where the critical element is the optimisation routine which is nonconvex and thus difficult to solve. A genetic algorithm based approach is proposed to deal with this problem. The efficiency of this approach had been demonstrated with a simulation example.