Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot

Tao Zhang; Jiang Long; Yangming Guo; Yaozhong Zhang

doi:10.1109/ICIEA58696.2023.10241889

Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot

Tao Zhang
, Jiang Long
, Yangming Guo
, Yaozhong Zhang

School of Cybersecurity

Northwestern Polytechnical University Xian
Beihang University

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

1 Scopus citations

Abstract

This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.

Original language	English
Title of host publication	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Editors	Wenjian Cai, Guilin Yang, Jun Qiu, Tingting Gao, Lijun Jiang, Tianjiang Zheng, Xinli Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	411-416
Number of pages	6
ISBN (Electronic)	9798350312201
DOIs	https://doi.org/10.1109/ICIEA58696.2023.10241889
State	Published - 2023
Event	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 - Ningbo, China Duration: 18 Aug 2023 → 22 Aug 2023

Publication series

Name	Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Conference

Conference	18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Country/Territory	China
City	Ningbo
Period	18/08/23 → 22/08/23

Keywords

adaptive tracking control
Concurrent learning
omnidirectional mobile robot
parameter identification

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10.1109/ICIEA58696.2023.10241889

Cite this

Zhang, T., Long, J., Guo, Y., & Zhang, Y. (2023). Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot. In W. Cai, G. Yang, J. Qiu, T. Gao, L. Jiang, T. Zheng, & X. Wang (Eds.), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 (pp. 411-416). (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA58696.2023.10241889

Zhang, Tao ; Long, Jiang ; Guo, Yangming et al. / Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. editor / Wenjian Cai ; Guilin Yang ; Jun Qiu ; Tingting Gao ; Lijun Jiang ; Tianjiang Zheng ; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 411-416 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

@inproceedings{6f3fc880d8a5423db502ee25c884c427,

title = "Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot",

abstract = "This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.",

keywords = "adaptive tracking control, Concurrent learning, omnidirectional mobile robot, parameter identification",

author = "Tao Zhang and Jiang Long and Yangming Guo and Yaozhong Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 ; Conference date: 18-08-2023 Through 22-08-2023",

year = "2023",

doi = "10.1109/ICIEA58696.2023.10241889",

language = "英语",

series = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "411--416",

editor = "Wenjian Cai and Guilin Yang and Jun Qiu and Tingting Gao and Lijun Jiang and Tianjiang Zheng and Xinli Wang",

booktitle = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",

}

Zhang, T, Long, J, Guo, Y & Zhang, Y 2023, Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot. in W Cai, G Yang, J Qiu, T Gao, L Jiang, T Zheng & X Wang (eds), Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 411-416, 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023, Ningbo, China, 18/08/23. https://doi.org/10.1109/ICIEA58696.2023.10241889

Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot. / Zhang, Tao; Long, Jiang; Guo, Yangming et al.
Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. ed. / Wenjian Cai; Guilin Yang; Jun Qiu; Tingting Gao; Lijun Jiang; Tianjiang Zheng; Xinli Wang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 411-416 (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023).

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

TY - GEN

T1 - Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot

AU - Zhang, Tao

AU - Long, Jiang

AU - Guo, Yangming

AU - Zhang, Yaozhong

PY - 2023

Y1 - 2023

N2 - This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.

AB - This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme.

KW - adaptive tracking control

KW - Concurrent learning

KW - omnidirectional mobile robot

KW - parameter identification

UR - https://www.scopus.com/pages/publications/85173612687

U2 - 10.1109/ICIEA58696.2023.10241889

DO - 10.1109/ICIEA58696.2023.10241889

M3 - 会议稿件

AN - SCOPUS:85173612687

T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

SP - 411

EP - 416

BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

A2 - Cai, Wenjian

A2 - Yang, Guilin

A2 - Qiu, Jun

A2 - Gao, Tingting

A2 - Jiang, Lijun

A2 - Zheng, Tianjiang

A2 - Wang, Xinli

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

Y2 - 18 August 2023 through 22 August 2023

ER -

Zhang T, Long J, Guo Y, Zhang Y. Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot. In Cai W, Yang G, Qiu J, Gao T, Jiang L, Zheng T, Wang X, editors, Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 411-416. (Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023). doi: 10.1109/ICIEA58696.2023.10241889

Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot

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