Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction

Shubo Wang; Xuemei Ren; Jing Na; Dongwu Li

doi:10.1109/ChiCC.2016.7553902

Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction

Shubo Wang, Xuemei Ren, Jing Na, Dongwu Li

School of Automation

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

8 Citations (Scopus)

Abstract

In this paper, a novel robust controller is proposed for servo mechanisms with nonlinear friction and external disturbance. First, a continuously differentiable friction model is used to represent the nonlinear friction, and neural network (NN) is employed to approximate the nonlinear friction and external disturabance. Then, a novel robust controller is designed by using robust integral of the sign of the error (RISE) term. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. The stability of closed-loop is proved based on Lyapunov stability theory, and all signal are proved to be bounded simultaneously. Finally, comparative simulations based on a turnable servo system are implemented to validate the efficacy of the proposed method.

Original language	English
Title of host publication	Proceedings of the 35th Chinese Control Conference, CCC 2016
Editors	Jie Chen, Qianchuan Zhao, Jie Chen
Publisher	IEEE Computer Society
Pages	3531-3536
Number of pages	6
ISBN (Electronic)	9789881563910
DOIs	https://doi.org/10.1109/ChiCC.2016.7553902
Publication status	Published - 26 Aug 2016
Event	35th Chinese Control Conference, CCC 2016 - Chengdu, China Duration: 27 Jul 2016 → 29 Jul 2016

Publication series

Name	Chinese Control Conference, CCC
Volume	2016-August
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	35th Chinese Control Conference, CCC 2016
Country/Territory	China
City	Chengdu
Period	27/07/16 → 29/07/16

Keywords

Servo mechanisms
desired compensation
friction compensation
robust integral of the sign of the error (RISE) term

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10.1109/ChiCC.2016.7553902

Cite this

Wang, S., Ren, X., Na, J., & Li, D. (2016). Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction. In J. Chen, Q. Zhao, & J. Chen (Eds.), Proceedings of the 35th Chinese Control Conference, CCC 2016 (pp. 3531-3536). Article 7553902 (Chinese Control Conference, CCC; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ChiCC.2016.7553902

@inproceedings{f54d61f26d394136bbd3731248a1be58,

title = "Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction",

abstract = "In this paper, a novel robust controller is proposed for servo mechanisms with nonlinear friction and external disturbance. First, a continuously differentiable friction model is used to represent the nonlinear friction, and neural network (NN) is employed to approximate the nonlinear friction and external disturabance. Then, a novel robust controller is designed by using robust integral of the sign of the error (RISE) term. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. The stability of closed-loop is proved based on Lyapunov stability theory, and all signal are proved to be bounded simultaneously. Finally, comparative simulations based on a turnable servo system are implemented to validate the efficacy of the proposed method.",

keywords = "Servo mechanisms, desired compensation, friction compensation, robust integral of the sign of the error (RISE) term",

author = "Shubo Wang and Xuemei Ren and Jing Na and Dongwu Li",

note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",

year = "2016",

month = aug,

day = "26",

doi = "10.1109/ChiCC.2016.7553902",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "3531--3536",

editor = "Jie Chen and Qianchuan Zhao and Jie Chen",

booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",

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}

Wang, S, Ren, X, Na, J & Li, D 2016, Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction. in J Chen, Q Zhao & J Chen (eds), Proceedings of the 35th Chinese Control Conference, CCC 2016., 7553902, Chinese Control Conference, CCC, vol. 2016-August, IEEE Computer Society, pp. 3531-3536, 35th Chinese Control Conference, CCC 2016, Chengdu, China, 27/07/16. https://doi.org/10.1109/ChiCC.2016.7553902

Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction. / Wang, Shubo; Ren, Xuemei; Na, Jing et al.
Proceedings of the 35th Chinese Control Conference, CCC 2016. ed. / Jie Chen; Qianchuan Zhao; Jie Chen. IEEE Computer Society, 2016. p. 3531-3536 7553902 (Chinese Control Conference, CCC; Vol. 2016-August).

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

TY - GEN

T1 - Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction

AU - Wang, Shubo

AU - Ren, Xuemei

AU - Na, Jing

AU - Li, Dongwu

PY - 2016/8/26

Y1 - 2016/8/26

N2 - In this paper, a novel robust controller is proposed for servo mechanisms with nonlinear friction and external disturbance. First, a continuously differentiable friction model is used to represent the nonlinear friction, and neural network (NN) is employed to approximate the nonlinear friction and external disturabance. Then, a novel robust controller is designed by using robust integral of the sign of the error (RISE) term. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. The stability of closed-loop is proved based on Lyapunov stability theory, and all signal are proved to be bounded simultaneously. Finally, comparative simulations based on a turnable servo system are implemented to validate the efficacy of the proposed method.

AB - In this paper, a novel robust controller is proposed for servo mechanisms with nonlinear friction and external disturbance. First, a continuously differentiable friction model is used to represent the nonlinear friction, and neural network (NN) is employed to approximate the nonlinear friction and external disturabance. Then, a novel robust controller is designed by using robust integral of the sign of the error (RISE) term. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. The stability of closed-loop is proved based on Lyapunov stability theory, and all signal are proved to be bounded simultaneously. Finally, comparative simulations based on a turnable servo system are implemented to validate the efficacy of the proposed method.

KW - Servo mechanisms

KW - desired compensation

KW - friction compensation

KW - robust integral of the sign of the error (RISE) term

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M3 - Conference contribution

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BT - Proceedings of the 35th Chinese Control Conference, CCC 2016

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Y2 - 27 July 2016 through 29 July 2016

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Robust Integral of Sign of Error and Neural Network Control for Servo System with Continuous Friction

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