Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints

Longnan Li; Zhengxiong Liu; Shaofan Guo; Zhiqiang Ma; Panfeng Huang

doi:10.1109/IECON49645.2022.9969103

Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints

Longnan Li, Zhengxiong Liu, Shaofan Guo, Zhiqiang Ma, Panfeng Huang

School of Astronautics

Northwestern Polytechnical University Xian

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

Abstract

In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9969103
State	Published - 2022
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Keywords

neural network
output constraints
prescribed-time control
teleoperation

Access to Document

10.1109/IECON49645.2022.9969103

Cite this

Li, L., Liu, Z., Guo, S., Ma, Z., & Huang, P. (2022). Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/IECON49645.2022.9969103

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title = "Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints",

abstract = "In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.",

keywords = "neural network, output constraints, prescribed-time control, teleoperation",

author = "Longnan Li and Zhengxiong Liu and Shaofan Guo and Zhiqiang Ma and Panfeng Huang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9969103",

language = "英语",

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

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booktitle = "IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society",

}

Li, L, Liu, Z, Guo, S, Ma, Z & Huang, P 2022, Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, IEEE Computer Society, 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9969103

Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints. / Li, Longnan; Liu, Zhengxiong; Guo, Shaofan et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2022. (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October).

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

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T1 - Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints

AU - Li, Longnan

AU - Liu, Zhengxiong

AU - Guo, Shaofan

AU - Ma, Zhiqiang

AU - Huang, Panfeng

PY - 2022

Y1 - 2022

N2 - In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.

AB - In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system's output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.

KW - neural network

KW - output constraints

KW - prescribed-time control

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T3 - IECON Proceedings (Industrial Electronics Conference)

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PB - IEEE Computer Society

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

Y2 - 17 October 2022 through 20 October 2022

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Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints

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