An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression
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 squa...
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| Veröffentlicht in: | Applied soft computing Jg. 73; S. 572 - 590 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.12.2018
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| ISSN: | 1568-4946, 1872-9681 |
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| 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.
•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. |
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| AbstractList | 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. |
| Author | Bououden, S. Boulkaibet, I. Chadli, M. Marwala, T. Belarbi, K. |
| Author_xml | – sequence: 1 givenname: I. surname: Boulkaibet fullname: Boulkaibet, I. email: ilyes@aims.ac.za organization: Institute for Intelligent Systems, University of Johannesburg, South Africa – sequence: 2 givenname: K. surname: Belarbi fullname: Belarbi, K. email: kbelarbi@yahoo.com organization: Department of Electronics, Faculty of Engineering, University of Constantine 1, Algeria – sequence: 3 givenname: S. surname: Bououden fullname: Bououden, S. email: ss_bououden@yahoo.fr organization: Faculty of Sciences and Technology, University Abbes Laghrour, Khenchela, Algeria – sequence: 4 givenname: M. surname: Chadli fullname: Chadli, M. email: mohammed.chadli@u-picardie.fr organization: University of Picardie Jules Verne Amiens, France – sequence: 5 givenname: T. surname: Marwala fullname: Marwala, T. email: tmarwala@uj.ac.za organization: Institute for Intelligent Systems, University of Johannesburg, South Africa |
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| Keywords | Takagi–Sugeno fuzzy system Least square support vector regression Fixed-budget kernel recursive least-squares Fuzzy c-means clustering Generalized predictive control |
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