An adaptive sliding mode controller based on online support vector regression for nonlinear systems

Kemal Uçak; Gülay Öke Günel

doi:10.1007/s00500-019-04223-9

An adaptive sliding mode 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

Mugla Sıtkı Kocman University

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

12 Citations (Scopus)

Abstract

In this paper, a novel adaptive sliding mode controller (SMC) based on support vector regression (SVR) is introduced for nonlinear systems. The closed-loop margin notion introduced for self-tuning regulators is rearranged in order to optimize the parameters of SMC. The proposed adjustment mechanism consists of an online SVR to identify the forward dynamics of the controlled system and SMC parameter estimators realized by separate online SVRs to approximate each tunable controller parameter. The performance of the proposed control architecture has been evaluated by simulations performed on a nonlinear continuously stirred tank reactor system, and the obtained results indicate that the SMC based on SVR provides robust and stable closed-loop performance.

Original language	English
Pages (from-to)	4623-4643
Number of pages	21
Journal	Soft Computing
Volume	24
Issue number	6
DOIs	https://doi.org/10.1007/s00500-019-04223-9
Publication status	Published - 1 Mar 2020

Bibliographical note

Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

Sliding mode control
Stability analysis
Support vector regression
SVR-based parameter estimator
SVR-based SMC

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10.1007/s00500-019-04223-9

Cite this

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abstract = "In this paper, a novel adaptive sliding mode controller (SMC) based on support vector regression (SVR) is introduced for nonlinear systems. The closed-loop margin notion introduced for self-tuning regulators is rearranged in order to optimize the parameters of SMC. The proposed adjustment mechanism consists of an online SVR to identify the forward dynamics of the controlled system and SMC parameter estimators realized by separate online SVRs to approximate each tunable controller parameter. The performance of the proposed control architecture has been evaluated by simulations performed on a nonlinear continuously stirred tank reactor system, and the obtained results indicate that the SMC based on SVR provides robust and stable closed-loop performance.",

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AU - Uçak, Kemal

AU - Öke Günel, Gülay

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N2 - In this paper, a novel adaptive sliding mode controller (SMC) based on support vector regression (SVR) is introduced for nonlinear systems. The closed-loop margin notion introduced for self-tuning regulators is rearranged in order to optimize the parameters of SMC. The proposed adjustment mechanism consists of an online SVR to identify the forward dynamics of the controlled system and SMC parameter estimators realized by separate online SVRs to approximate each tunable controller parameter. The performance of the proposed control architecture has been evaluated by simulations performed on a nonlinear continuously stirred tank reactor system, and the obtained results indicate that the SMC based on SVR provides robust and stable closed-loop performance.

AB - In this paper, a novel adaptive sliding mode controller (SMC) based on support vector regression (SVR) is introduced for nonlinear systems. The closed-loop margin notion introduced for self-tuning regulators is rearranged in order to optimize the parameters of SMC. The proposed adjustment mechanism consists of an online SVR to identify the forward dynamics of the controlled system and SMC parameter estimators realized by separate online SVRs to approximate each tunable controller parameter. The performance of the proposed control architecture has been evaluated by simulations performed on a nonlinear continuously stirred tank reactor system, and the obtained results indicate that the SMC based on SVR provides robust and stable closed-loop performance.

KW - Sliding mode control

KW - Stability analysis

KW - Support vector regression

KW - SVR-based parameter estimator

KW - SVR-based SMC

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An adaptive sliding mode controller based on online support vector regression for nonlinear systems

Abstract

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Keywords

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