Active-disturbance rejection control of brushless DC motor based on BP neural network

Zhi Liu; Hong Guo; Dayu Wang; Zhiyong Wu; Jinquan Xu

doi:10.1109/iCECE.2010.794

Active-disturbance rejection control of brushless DC motor based on BP neural network

Zhi Liu^*
, Hong Guo
, Dayu Wang
, Zhiyong Wu
, Jinquan Xu

^*Corresponding author for this work

Beihang University

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

10 Scopus citations

Abstract

Brushless DC motor speed servo system is multivariable, nonlinear and strong coupling. Its performance is easily influenced by the parameter variation, the cogging torque and the load disturbance. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control (ADRC) based on back-propagation (BP) neural network. The ADRC is independent of accurate system and its extended-state observer can estimate the disturbance of the system accurately. However, the parameters of Nonlinear Feedback (NF) in ADRC are difficult to obtain. In this paper, these parameters are self-turned by the BP neural network. The simulation results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity, control accuracy, adaptability and robustness.

Original language	English
Title of host publication	Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010
Pages	3253-3256
Number of pages	4
DOIs	https://doi.org/10.1109/iCECE.2010.794
State	Published - 2010
Event	International Conference on Electrical and Control Engineering, ICECE 2010 - Wuhan, China Duration: 26 Jun 2010 → 28 Jun 2010

Publication series

Name	Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010

Conference

Conference	International Conference on Electrical and Control Engineering, ICECE 2010
Country/Territory	China
City	Wuhan
Period	26/06/10 → 28/06/10

Keywords

ADRC (active-disturbance rejection control)
BP (back propagation algorithms)
Brushless DC motor (BLDCM)
Parameters self-turning

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10.1109/iCECE.2010.794

Cite this

Liu, Z., Guo, H., Wang, D., Wu, Z., & Xu, J. (2010). Active-disturbance rejection control of brushless DC motor based on BP neural network. In Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010 (pp. 3253-3256). Article 5629914 (Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010). https://doi.org/10.1109/iCECE.2010.794

@inproceedings{46e611099963461392e306ea21f389ce,

title = "Active-disturbance rejection control of brushless DC motor based on BP neural network",

abstract = "Brushless DC motor speed servo system is multivariable, nonlinear and strong coupling. Its performance is easily influenced by the parameter variation, the cogging torque and the load disturbance. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control (ADRC) based on back-propagation (BP) neural network. The ADRC is independent of accurate system and its extended-state observer can estimate the disturbance of the system accurately. However, the parameters of Nonlinear Feedback (NF) in ADRC are difficult to obtain. In this paper, these parameters are self-turned by the BP neural network. The simulation results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity, control accuracy, adaptability and robustness.",

keywords = "ADRC (active-disturbance rejection control), BP (back propagation algorithms), Brushless DC motor (BLDCM), Parameters self-turning",

author = "Zhi Liu and Hong Guo and Dayu Wang and Zhiyong Wu and Jinquan Xu",

year = "2010",

doi = "10.1109/iCECE.2010.794",

language = "英语",

isbn = "9780769540313",

series = "Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010",

pages = "3253--3256",

booktitle = "Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010",

note = "International Conference on Electrical and Control Engineering, ICECE 2010 ; Conference date: 26-06-2010 Through 28-06-2010",

}

Liu, Z, Guo, H, Wang, D, Wu, Z & Xu, J 2010, Active-disturbance rejection control of brushless DC motor based on BP neural network. in Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010., 5629914, Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010, pp. 3253-3256, International Conference on Electrical and Control Engineering, ICECE 2010, Wuhan, China, 26/06/10. https://doi.org/10.1109/iCECE.2010.794

Active-disturbance rejection control of brushless DC motor based on BP neural network. / Liu, Zhi; Guo, Hong; Wang, Dayu et al.
Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010. 2010. p. 3253-3256 5629914 (Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010).

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

TY - GEN

T1 - Active-disturbance rejection control of brushless DC motor based on BP neural network

AU - Liu, Zhi

AU - Guo, Hong

AU - Wang, Dayu

AU - Wu, Zhiyong

AU - Xu, Jinquan

PY - 2010

Y1 - 2010

N2 - Brushless DC motor speed servo system is multivariable, nonlinear and strong coupling. Its performance is easily influenced by the parameter variation, the cogging torque and the load disturbance. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control (ADRC) based on back-propagation (BP) neural network. The ADRC is independent of accurate system and its extended-state observer can estimate the disturbance of the system accurately. However, the parameters of Nonlinear Feedback (NF) in ADRC are difficult to obtain. In this paper, these parameters are self-turned by the BP neural network. The simulation results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity, control accuracy, adaptability and robustness.

AB - Brushless DC motor speed servo system is multivariable, nonlinear and strong coupling. Its performance is easily influenced by the parameter variation, the cogging torque and the load disturbance. To solve the deficiency, the paper represents the algorithm of active-disturbance rejection control (ADRC) based on back-propagation (BP) neural network. The ADRC is independent of accurate system and its extended-state observer can estimate the disturbance of the system accurately. However, the parameters of Nonlinear Feedback (NF) in ADRC are difficult to obtain. In this paper, these parameters are self-turned by the BP neural network. The simulation results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity, control accuracy, adaptability and robustness.

KW - ADRC (active-disturbance rejection control)

KW - BP (back propagation algorithms)

KW - Brushless DC motor (BLDCM)

KW - Parameters self-turning

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U2 - 10.1109/iCECE.2010.794

DO - 10.1109/iCECE.2010.794

M3 - 会议稿件

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SN - 9780769540313

T3 - Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010

SP - 3253

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BT - Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010

T2 - International Conference on Electrical and Control Engineering, ICECE 2010

Y2 - 26 June 2010 through 28 June 2010

ER -

Active-disturbance rejection control of brushless DC motor based on BP neural network

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