Generalized Predictive Control Method for a Class of Nonlinear Systems Using ANFIS and Multiple Models

This paper develops a generalized predictive control method using adaptive-network-based fuzzy inference system (ANFIS) and multiple models for a class of uncertain discrete-time nonlinear systems with unstable zero-dynamics. The proposed method is composed of a linear robust generalized predictive controller, an ANFIS-based nonlinear generalized predictive controller, and a switching mechanism using multiple models technique. The method in this paper has the following three features compared with the results available in the literature. First, this method relaxes the global boundedness assumption of the unmodelled dynamics in the literature, and thus widens its ranges of applications. Secondly, the ANFIS is used to estimate and compensate for the unmodeled dynamics adaptively in the nonlinear generalized predictive controller design, which successfully tackles the relatively low convergence rate of neural networks and avoids the possibility that the network becomes trapped in local minima. Thirdly, to guarantee the universal approximation property of ANFIS, a "one-to-one mapping" is adapted. A simulation example is exploited to illustrate the effectiveness of the proposed method.