An adaptive fuzzy predictive control of nonlinear processes based on Multi Kernel least squares support vector regression
Abstract In this paper, an adaptive fuzzy Generalized Predictive Control (GPC) is proposed for discrete time nonlinear systems via Takagi Sugeno system based Multi Kernel Least Squares Support Vector Regression (TS LSSVR) The proposed adaptive TS LSSVR strategy is constructed using a multi kernel least squares support vector regression where the learning procedure of the proposed TS LSSVR is achieved in three steps: In the first step, which is an offline step, the antecedent parameters of the TS LSSVR are initialized using a fuzzy c means clustering algorithm. The second step, which is an online step, deals with the adaptation of the antecedent parameters which can be implemented using a back propagation algorithm. Finally, the last online step is to use the Fixed Budget Kernel Recursive Least Squares algorithm to obtain the consequent parameters. Furthermore, an adaptive generalized predictive control for nonlinear systems is introduced by integrating the proposed adaptive TS LSSVR into the generalized predictive controller (GPC) The reliability of the proposed adaptive TS LSSVR GPC controller is investigated by controlling two nonlinear systems: A surge tank and continuous stirred tank reactor (CSTR) systems. The proposed TS LSSVR GPC controller has demonstrated good results and efficiently controlled the nonlinear plants. Furthermore, the adaptive TS LSSVR GPC has the ability to deal with disturbances and variations in the nonlinear systems.
Appl. Soft Comput.