Sliding mode control with neural network for active magnetic bearing system

Zhi Cao; Jianning Dong; Faisal Wani; Henk Polinder; Pavol Bauer; Fei Peng; Yunkai Huang

doi:10.1109/IECON.2019.8926808

Sliding mode control with neural network for active magnetic bearing system

Zhi Cao, Jianning Dong, Faisal Wani, Henk Polinder, Pavol Bauer, Fei Peng, Yunkai Huang

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

82 Downloads (Pure)

Abstract

A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	744-749
Number of pages	6
ISBN (Electronic)	9781728148786
ISBN (Print)	978-1-7281-4878-6
DOIs	https://doi.org/10.1109/IECON.2019.8926808
Publication status	Published - 2019
Event	IECON 2019: IEEE 45th Annual Conference of the Industrial Electronics Society - Lisbon, Portugal Duration: 14 Oct 2019 → 17 Oct 2019 Conference number: 45th

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	14/10/19 → 17/10/19

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

magnetic bearing
neural network
sliding mode control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IECON.2019.8926808

Sliding_Mode_Control_with_Neural_Network_for_Active_Magnetic_Bearing_SystemFinal published version, 3.2 MB

Cite this

Cao, Z., Dong, J., Wani, F., Polinder, H., Bauer, P., Peng, F., & Huang, Y. (2019). Sliding mode control with neural network for active magnetic bearing system. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 744-749). Article 8926808 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE. https://doi.org/10.1109/IECON.2019.8926808

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title = "Sliding mode control with neural network for active magnetic bearing system",

abstract = "A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.",

keywords = "magnetic bearing, neural network, sliding mode control",

author = "Zhi Cao and Jianning Dong and Faisal Wani and Henk Polinder and Pavol Bauer and Fei Peng and Yunkai Huang",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; IECON 2019 : IEEE 45th Annual Conference of the Industrial Electronics Society ; Conference date: 14-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1109/IECON.2019.8926808",

language = "English",

isbn = "978-1-7281-4878-6",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE",

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Cao, Z, Dong, J, Wani, F, Polinder, H , Bauer, P, Peng, F & Huang, Y 2019, Sliding mode control with neural network for active magnetic bearing system. in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8926808, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE, Piscataway, NJ, USA, pp. 744-749, IECON 2019, Lisbon, Portugal, 14/10/19. https://doi.org/10.1109/IECON.2019.8926808

Sliding mode control with neural network for active magnetic bearing system. / Cao, Zhi; Dong, Jianning; Wani, Faisal et al.
Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ, USA: IEEE, 2019. p. 744-749 8926808 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Sliding mode control with neural network for active magnetic bearing system

AU - Cao, Zhi

AU - Dong, Jianning

AU - Wani, Faisal

AU - Polinder, Henk

AU - Bauer, Pavol

AU - Peng, Fei

AU - Huang, Yunkai

N1 - Conference code: 45th

PY - 2019

Y1 - 2019

N2 - A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.

AB - A novel controller design procedure is proposed for a 5-degree-of-freedom (DOF) active magnetic bearing (AMB) system, based on sliding mode control (SMC) and neural network (NN). The SMC is used to achieve high robustness and fast response while the NN can compensate unmodeled uncertainty and external disturbance by on-line tuning algorithm. The proposed controller is compared with the well-tuned PID controller by simulations. The simulation results show the superior performance of the proposed controller.

KW - magnetic bearing

KW - neural network

KW - sliding mode control

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DO - 10.1109/IECON.2019.8926808

M3 - Conference contribution

SN - 978-1-7281-4878-6

T3 - IECON Proceedings (Industrial Electronics Conference)

SP - 744

EP - 749

BT - Proceedings

PB - IEEE

CY - Piscataway, NJ, USA

T2 - IECON 2019

Y2 - 14 October 2019 through 17 October 2019

ER -

Cao Z, Dong J, Wani F, Polinder H , Bauer P, Peng F et al. Sliding mode control with neural network for active magnetic bearing system. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ, USA: IEEE. 2019. p. 744-749. 8926808. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON.2019.8926808

Sliding mode control with neural network for active magnetic bearing system

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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