The design of a nonlinear predictive controller, based on a fuzzy model is presented. The Takagi -Sugeno fuzzy model with an adaptive neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is simulated and applied to the water level control in the U-tube steam generating unit (UTSG) used for electricity generation. The control experiments were successfully conducted for this nonlinear process with satisfactory results and performances.
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