A fuzzy generalized predictive control using affine fuzzy predictors for nonlinear systems

Abstract

In this paper a fuzzy generalized predictive control (FGPC) for nonlinear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A modified parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus could possess inherent good properties of the GPC. It is shown by computer simulation that the performance of the FGPC is satisfactory.