Generalized Predictive Control of a thermal plant using fuzzy model

In the paper a new approach to predictive control of highly nonlinear processes based on Takagi-Sugeno fuzzy model is proposed. It is investigated how the Takagi-Sugeno fuzzy models can be linked to a special type of model based predictive control algorithm, the Generalized Predictive Control (GPC). In original GPC design purely linear transfer function model is used for long-range prediction. The advantage of GPC and other linear Model Based Predictive Control (MBPC) methods is in guaranteed convergence within each time sample, but they are not able to deal with strong process nonlinearities. In our approach, approximate linear models are extracted at each time sample by instantaneous linearization of nonlinear fuzzy model and adaptive GPC is used. Applicability of this approach to control a real world process (nonlinear laboratory-scale thermal plant) with operating point dependent gain and time constants is demonstrated in the paper.