Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm

Shaohua Niu; Wencai Zhang; Tianzhen Li; Gan Zhan

doi:10.1007/978-3-031-13844-7_21

Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm

Shaohua Niu, Wencai Zhang, Tianzhen Li, Gan Zhan^*

^*Corresponding author for this work

School of Mechatronical Engineering

Beijing Institute of Technology

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

Abstract

This paper designs an automatic calibration method and system of motor torque for the problem of low loading accuracy of motor torque. The system uses genetic algorithm to optimize PID parameters and load control and measurement of the motor. The genetic algorithm is realized in the simulation platform, and the iterative operation is carried out by setting different cross probability and mutation probability parameters. The results are substituted into the motor model to analyze the response speed and anti-interference ability of the motor to the given random signal, and the optimal PID parameters are obtained as the configuration parameters of the motor torque automatic calibration system. The experimental results show that compared with the traditional motor torque calibration loading control, the accuracy of the system torque calibration error is improved and the error range is controlled within ±0.003 N · m, which verifies the effectiveness and feasibility of this method.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings
Editors	Honghai Liu, Weihong Ren, Zhouping Yin, Lianqing Liu, Li Jiang, Guoying Gu, Xinyu Wu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	209-217
Number of pages	9
ISBN (Print)	9783031138430
DOIs	https://doi.org/10.1007/978-3-031-13844-7_21
Publication status	Published - 2022
Event	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 - Harbin, China Duration: 1 Aug 2022 → 3 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13455 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Robotics and Applications, ICIRA 2022
Country/Territory	China
City	Harbin
Period	1/08/22 → 3/08/22

Keywords

Anti interference ability
Genetic algorithms
Loading control
Motor torque calibration accuracy
PID parameters
Response speed

Access to Document

10.1007/978-3-031-13844-7_21

Cite this

Niu, S., Zhang, W., Li, T., & Zhan, G. (2022). Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm. In H. Liu, W. Ren, Z. Yin, L. Liu, L. Jiang, G. Gu, & X. Wu (Eds.), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings (pp. 209-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-13844-7_21

Niu, Shaohua ; Zhang, Wencai ; Li, Tianzhen et al. / Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm. Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. editor / Honghai Liu ; Weihong Ren ; Zhouping Yin ; Lianqing Liu ; Li Jiang ; Guoying Gu ; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 209-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm",

abstract = "This paper designs an automatic calibration method and system of motor torque for the problem of low loading accuracy of motor torque. The system uses genetic algorithm to optimize PID parameters and load control and measurement of the motor. The genetic algorithm is realized in the simulation platform, and the iterative operation is carried out by setting different cross probability and mutation probability parameters. The results are substituted into the motor model to analyze the response speed and anti-interference ability of the motor to the given random signal, and the optimal PID parameters are obtained as the configuration parameters of the motor torque automatic calibration system. The experimental results show that compared with the traditional motor torque calibration loading control, the accuracy of the system torque calibration error is improved and the error range is controlled within ±0.003 N · m, which verifies the effectiveness and feasibility of this method.",

keywords = "Anti interference ability, Genetic algorithms, Loading control, Motor torque calibration accuracy, PID parameters, Response speed",

author = "Shaohua Niu and Wencai Zhang and Tianzhen Li and Gan Zhan",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",

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editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",

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Niu, S, Zhang, W, Li, T & Zhan, G 2022, Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm. in H Liu, W Ren, Z Yin, L Liu, L Jiang, G Gu & X Wu (eds), Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13455 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 209-217, 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022, Harbin, China, 1/08/22. https://doi.org/10.1007/978-3-031-13844-7_21

Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm. / Niu, Shaohua; Zhang, Wencai; Li, Tianzhen et al.
Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. ed. / Honghai Liu; Weihong Ren; Zhouping Yin; Lianqing Liu; Li Jiang; Guoying Gu; Xinyu Wu. Springer Science and Business Media Deutschland GmbH, 2022. p. 209-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13455 LNAI).

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

TY - GEN

T1 - Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm

AU - Niu, Shaohua

AU - Zhang, Wencai

AU - Li, Tianzhen

AU - Zhan, Gan

PY - 2022

Y1 - 2022

N2 - This paper designs an automatic calibration method and system of motor torque for the problem of low loading accuracy of motor torque. The system uses genetic algorithm to optimize PID parameters and load control and measurement of the motor. The genetic algorithm is realized in the simulation platform, and the iterative operation is carried out by setting different cross probability and mutation probability parameters. The results are substituted into the motor model to analyze the response speed and anti-interference ability of the motor to the given random signal, and the optimal PID parameters are obtained as the configuration parameters of the motor torque automatic calibration system. The experimental results show that compared with the traditional motor torque calibration loading control, the accuracy of the system torque calibration error is improved and the error range is controlled within ±0.003 N · m, which verifies the effectiveness and feasibility of this method.

AB - This paper designs an automatic calibration method and system of motor torque for the problem of low loading accuracy of motor torque. The system uses genetic algorithm to optimize PID parameters and load control and measurement of the motor. The genetic algorithm is realized in the simulation platform, and the iterative operation is carried out by setting different cross probability and mutation probability parameters. The results are substituted into the motor model to analyze the response speed and anti-interference ability of the motor to the given random signal, and the optimal PID parameters are obtained as the configuration parameters of the motor torque automatic calibration system. The experimental results show that compared with the traditional motor torque calibration loading control, the accuracy of the system torque calibration error is improved and the error range is controlled within ±0.003 N · m, which verifies the effectiveness and feasibility of this method.

KW - Anti interference ability

KW - Genetic algorithms

KW - Loading control

KW - Motor torque calibration accuracy

KW - PID parameters

KW - Response speed

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DO - 10.1007/978-3-031-13844-7_21

M3 - Conference contribution

AN - SCOPUS:85135825049

SN - 9783031138430

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

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EP - 217

BT - Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings

A2 - Liu, Honghai

A2 - Ren, Weihong

A2 - Yin, Zhouping

A2 - Liu, Lianqing

A2 - Jiang, Li

A2 - Gu, Guoying

A2 - Wu, Xinyu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022

Y2 - 1 August 2022 through 3 August 2022

ER -

Niu S, Zhang W, Li T, Zhan G. Optimal Control Method of Motor Torque Loading Based on Genetic Algorithm. In Liu H, Ren W, Yin Z, Liu L, Jiang L, Gu G, Wu X, editors, Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 209-217. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-13844-7_21