Design of an adaptive support vector regressor controller for a spherical tank system

Kemal Uçak; Gülay Öke Günel

doi:10.1007/978-3-319-26555-1_1

Design of an adaptive support vector regressor controller for a spherical tank system

Kemal Uçak^*, Gülay Öke Günel

^*Corresponding author for this work

Department of Control and Automation Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, an adaptive support vector regressor (SVR) controller which has previously been proposed [1]is applied to control the liquid level in a spherical tank system. The variations in the cross sectional area of the tank depending on the liquid level is the main cause of nonlinearity in system. The parameters of the controller are optimized depending on the future behaviour of the system which is approximated via a seperate online SVR model of the system. In order to adjust controller parameters, the “closed-loop margin” which is calculated using the tracking error has been optimized. The performance of the proposed method has been examined by simulations carried out on a nonlinear spherical tank system, and the results reveal that the SVR controller together with SVR model leads to good tracking performance with small modeling, transient state and steady state errors.

Original language	English
Title of host publication	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Editors	Tingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
Publisher	Springer Verlag
Pages	1-8
Number of pages	8
ISBN (Print)	9783319265544
DOIs	https://doi.org/10.1007/978-3-319-26555-1_1
Publication status	Published - 2015
Event	22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9491
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Neural Information Processing, ICONIP 2015
Country/Territory	Turkey
City	Istanbul
Period	9/11/15 → 12/11/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Model based adaptive control
Online support vector regression
Spherical tank system
SVR controller
SVR model identification

Access to Document

10.1007/978-3-319-26555-1_1

Cite this

Uçak, K., & Öke Günel, G. (2015). Design of an adaptive support vector regressor controller for a spherical tank system. In T. Huang, Q. Liu, W. K. Lai, & S. Arik (Eds.), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings (pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491). Springer Verlag. https://doi.org/10.1007/978-3-319-26555-1_1

Uçak, Kemal ; Öke Günel, Gülay. / Design of an adaptive support vector regressor controller for a spherical tank system. Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. editor / Tingwen Huang ; Qingshan Liu ; Weng Kin Lai ; Sabri Arik. Springer Verlag, 2015. pp. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this study, an adaptive support vector regressor (SVR) controller which has previously been proposed [1]is applied to control the liquid level in a spherical tank system. The variations in the cross sectional area of the tank depending on the liquid level is the main cause of nonlinearity in system. The parameters of the controller are optimized depending on the future behaviour of the system which is approximated via a seperate online SVR model of the system. In order to adjust controller parameters, the “closed-loop margin” which is calculated using the tracking error has been optimized. The performance of the proposed method has been examined by simulations carried out on a nonlinear spherical tank system, and the results reveal that the SVR controller together with SVR model leads to good tracking performance with small modeling, transient state and steady state errors.",

keywords = "Model based adaptive control, Online support vector regression, Spherical tank system, SVR controller, SVR model identification",

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

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

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doi = "10.1007/978-3-319-26555-1_1",

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Uçak, K & Öke Günel, G 2015, Design of an adaptive support vector regressor controller for a spherical tank system. in T Huang, Q Liu, WK Lai & S Arik (eds), Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9491, Springer Verlag, pp. 1-8, 22nd International Conference on Neural Information Processing, ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26555-1_1

Design of an adaptive support vector regressor controller for a spherical tank system. / Uçak, Kemal; Öke Günel, Gülay.
Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. ed. / Tingwen Huang; Qingshan Liu; Weng Kin Lai; Sabri Arik. Springer Verlag, 2015. p. 1-8 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Öke Günel, Gülay

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N2 - In this study, an adaptive support vector regressor (SVR) controller which has previously been proposed [1]is applied to control the liquid level in a spherical tank system. The variations in the cross sectional area of the tank depending on the liquid level is the main cause of nonlinearity in system. The parameters of the controller are optimized depending on the future behaviour of the system which is approximated via a seperate online SVR model of the system. In order to adjust controller parameters, the “closed-loop margin” which is calculated using the tracking error has been optimized. The performance of the proposed method has been examined by simulations carried out on a nonlinear spherical tank system, and the results reveal that the SVR controller together with SVR model leads to good tracking performance with small modeling, transient state and steady state errors.

AB - In this study, an adaptive support vector regressor (SVR) controller which has previously been proposed [1]is applied to control the liquid level in a spherical tank system. The variations in the cross sectional area of the tank depending on the liquid level is the main cause of nonlinearity in system. The parameters of the controller are optimized depending on the future behaviour of the system which is approximated via a seperate online SVR model of the system. In order to adjust controller parameters, the “closed-loop margin” which is calculated using the tracking error has been optimized. The performance of the proposed method has been examined by simulations carried out on a nonlinear spherical tank system, and the results reveal that the SVR controller together with SVR model leads to good tracking performance with small modeling, transient state and steady state errors.

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Uçak K, Öke Günel G. Design of an adaptive support vector regressor controller for a spherical tank system. In Huang T, Liu Q, Lai WK, Arik S, editors, Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings. Springer Verlag. 2015. p. 1-8. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-26555-1_1

Design of an adaptive support vector regressor controller for a spherical tank system

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Keywords

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