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An adaptive support vector regressor controller for nonlinear systems

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Abstract

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.

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Authors and Affiliations

  1. Department of Control and Automation Engineering, Faculty of Electrical-Electronics Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey

    Kemal Uçak & Gülay Öke Günel

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  1. Kemal Uçak
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  2. Gülay Öke Günel
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Correspondence to Kemal Uçak.

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Communicated by V. Loia.

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Uçak, K., Öke Günel, G. An adaptive support vector regressor controller for nonlinear systems. Soft Comput 20, 2531–2556 (2016). https://doi.org/10.1007/s00500-015-1654-0

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  • DOI: https://doi.org/10.1007/s00500-015-1654-0

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