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

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Abstract

In this study, a novel nonlinear autoregressive moving average (NARMA)-L2 controller based on online support vector regression (SVR) is proposed. The main idea is to obtain a SVR based NARMA-L2 model of a nonlinear single input single output system (SISO) by decomposing a single SVR which estimates the nonlinear autoregressive with exogenous inputs (NARX) model of the system. Consequently, using the obtained SVR-NARMA-L2 submodels, a NARMA-L2 controller is designed. The performance of the proposed SVR based NARMA-L2 controller has been evaluated by simulations carried out on a bioreactor system, and the results show that the SVR based NARMA-L2 model and controller attain good modelling and control performances. Robustness of the controller in the case of system parameter uncertainty and measurement noise have also been examined.

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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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Uçak, K., Öke Günel, G. A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems. Neural Process Lett 44, 857–886 (2016). https://doi.org/10.1007/s11063-016-9500-7

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