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

Kemal Uçak; Gülay Öke Günel

doi:10.1007/s11063-016-9500-7

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

Kemal Uçak^*, Gülay Öke Günel

^*Corresponding author for this work

Department of Control and Automation Engineering

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

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.

Original language	English
Pages (from-to)	857-886
Number of pages	30
Journal	Neural Processing Letters
Volume	44
Issue number	3
DOIs	https://doi.org/10.1007/s11063-016-9500-7
Publication status	Published - 1 Dec 2016

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

Keywords

Adaptive control
NARMA-L2 controller
NARMA-L2 model
Online support vector regression
SVR-NARMA-L2 controller

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10.1007/s11063-016-9500-7

Cite this

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

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.",

keywords = "Adaptive control, NARMA-L2 controller, NARMA-L2 model, Online support vector regression, SVR-NARMA-L2 controller",

author = "Kemal U{\c c}ak and {{\"O}ke G{\"u}nel}, G{\"u}lay",

note = "Publisher Copyright: {\textcopyright} 2016, Springer Science+Business Media New York.",

year = "2016",

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doi = "10.1007/s11063-016-9500-7",

language = "English",

volume = "44",

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T1 - A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems

AU - Uçak, Kemal

AU - Öke Günel, Gülay

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

KW - Adaptive control

KW - NARMA-L2 controller

KW - NARMA-L2 model

KW - Online support vector regression

KW - SVR-NARMA-L2 controller

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JO - Neural Processing Letters

JF - Neural Processing Letters

IS - 3

ER -

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

Abstract

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Keywords

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