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Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems

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Abstract

NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the precision with which controlled systems’ dynamics can be estimated. In this paper, online SVR is utilized to obtain controlled plant’s subdynamics and consequently this information is used in the construction of NARMA-L2 controller. Hence functionality of NARMA-L2 controllers and high generalization capability of SVR are combined. Also, SVR formulates a convex optimization problem and therefore guarantees global optimum solution. The proposed method is assessed by performing simulations on a nonlinear CSTR system, the robustness of the designed controller is also tested under noisy and uncertainty conditions.

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Authors and Affiliations

  1. Massachusetts Institute of Technology Cambridge, MA, 02139, USA

    Kemal Uçak

  2. Muğla Sıtkı  Koçman University Kötekli, 48000, Muğla, Turkey

    Kemal Uçak

  3. Istanbul Technical University Maslak, 34469, Istanbul, Turkey

    Gülay Öke Günel

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

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Uçak, K., Günel, G.Ö. Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems. Neural Process Lett 53, 405–428 (2021). https://doi.org/10.1007/s11063-020-10403-8

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