An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression - Boulkaibet, I.; Belarbi, K.; Bououden, S.; Chadli, M.; Marwala, T. (Applied soft computing, 2018)
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. •An adaptive TS system based on LSSVR was introduced for system identification.•The adaptive TS-LSSVR was integrated within the generalized predictive controller.•The proposed adaptive TS-LSSVR GPC controller was used to control nonlinear systems.•The adaptive TS-LSSVR GPC has shown good performance in controlling nonlinear systems.