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

^*Bu çalışma için yazışmadan sorumlu yazar

Kontrol ve Otomasyon Mühendisliği Bölümü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
Editörler	Tingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
Yayınlayan	Springer Verlag
Sayfalar	1-8
Sayfa sayısı	8
ISBN (Basılı)	9783319265544
DOI'lar	https://doi.org/10.1007/978-3-319-26555-1_1
Yayın durumu	Yayınlandı - 2015
Etkinlik	22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey Süre: 9 Kas 2015 → 12 Kas 2015

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	9491
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	22nd International Conference on Neural Information Processing, ICONIP 2015
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	9/11/15 → 12/11/15

Bibliyografik not

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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); Hac. 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. editör / 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)).

@inproceedings{4c725976c8124b5caef4af152e2909c0,

title = "Design of an adaptive support vector regressor controller for a spherical tank system",

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, SVR controller, SVR model identification, Spherical tank system",

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",

year = "2015",

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

language = "English",

isbn = "9783319265544",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1--8",

editor = "Tingwen Huang and Qingshan Liu and Lai, \{Weng Kin\} and Sabri Arik",

booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",

address = "Germany",

}

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), hac. 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); Hac. 9491).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Design of an adaptive support vector regressor controller for a spherical tank system

AU - Uçak, Kemal

AU - Öke Günel, Gülay

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

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.

KW - Model based adaptive control

KW - Online support vector regression

KW - SVR controller

KW - SVR model identification

KW - Spherical tank system

UR - https://www.scopus.com/pages/publications/84958541978

U2 - 10.1007/978-3-319-26555-1_1

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

M3 - Conference contribution

AN - SCOPUS:84958541978

SN - 9783319265544

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 8

BT - Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings

A2 - Huang, Tingwen

A2 - Liu, Qingshan

A2 - Lai, Weng Kin

A2 - Arik, Sabri

PB - Springer Verlag

T2 - 22nd International Conference on Neural Information Processing, ICONIP 2015

Y2 - 9 November 2015 through 12 November 2015

ER -

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, editörler, 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

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

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