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Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach

Dechao Chen, Shuai Li Orcid Logo, Qing Wu, Liefa Liao

Neurocomputing, Volume: 381, Pages: 282 - 297

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.neucom.2019.11.031

Abstract

Previous works of traditional zeroing neural networks (or termed Zhang neural networks, ZNN) show great success for solving specific time-variant problems of known systems in an ideal environment. However, it is still a challenging issue for the ZNN to effectively solve time-variant problems for unc...

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Published in: Neurocomputing
ISSN: 0925-2312 1872-8286
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa52966
first_indexed 2019-12-05T13:16:14Z
last_indexed 2025-04-10T05:36:27Z
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spelling 2025-04-09T14:35:27.2633391 v2 52966 2019-12-05 Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2019-12-05 ACEM Previous works of traditional zeroing neural networks (or termed Zhang neural networks, ZNN) show great success for solving specific time-variant problems of known systems in an ideal environment. However, it is still a challenging issue for the ZNN to effectively solve time-variant problems for uncertain systems without the prior knowledge. Simultaneously, the involvement of external disturbances in the neural network model makes it even hard for time-variant problem solving due to the intensively computational burden and low accuracy. In this paper, a unified neural approach of simultaneous identification, tracking control and disturbance rejection in the framework of the ZNN is proposed to address the time-variant tracking control of uncertain nonlinear dynamics systems (UNDS). The neural network model derived by the proposed approach captures hidden relations between inputs and outputs of the UNDS. The proposed model shows outstanding tracking performance even under the influences of uncertainties and disturbances. Then, the continuous-time model is discretized via Euler forward formula (EFF). The corresponding discrete algorithm and block diagram are also presented for the convenience of implementation. Theoretical analyses on the convergence property and discretization accuracy are presented to verify the performance of the neural network model. Finally, numerical studies, robot applications, performance comparisons and tests demonstrate the effectiveness and advantages of the proposed neural network model for the time-variant tracking control of UNDS. Journal Article Neurocomputing 381 282 297 Elsevier BV 0925-2312 1872-8286 Zhang neural netowrks (ZNN), Time-variant tracking control, Time-variant problems, Robustness, Identification 14 3 2020 2020-03-14 10.1016/j.neucom.2019.11.031 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Not Required This work is supported by the National Natural Science Foundation of China (with numbers 61906054, 61401385 and 61702146), by Hong Kong Research Grants Council Early Career Scheme (with number 25214015), by Departmental General Research Fund of Hong Kong Polytechnic University (with number G.61.37.UA7L), and also by PolyU Central Research Grant (with number G-YBMU). 2025-04-09T14:35:27.2633391 2019-12-05T10:38:39.5849552 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Dechao Chen 1 Shuai Li 0000-0001-8316-5289 2 Qing Wu 3 Liefa Liao 4 52966__16451__054306bc0f2142fe8a8d646ba92a2503.pdf 52966.pdf 2020-01-27T12:42:51.4994419 Output 887528 application/pdf Accepted Manuscript true 2020-12-04T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
spellingShingle Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
Shuai Li
title_short Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
title_full Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
title_fullStr Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
title_full_unstemmed Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
title_sort Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Shuai Li
Qing Wu
Liefa Liao
format Journal article
container_title Neurocomputing
container_volume 381
container_start_page 282
publishDate 2020
institution Swansea University
issn 0925-2312
1872-8286
doi_str_mv 10.1016/j.neucom.2019.11.031
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Previous works of traditional zeroing neural networks (or termed Zhang neural networks, ZNN) show great success for solving specific time-variant problems of known systems in an ideal environment. However, it is still a challenging issue for the ZNN to effectively solve time-variant problems for uncertain systems without the prior knowledge. Simultaneously, the involvement of external disturbances in the neural network model makes it even hard for time-variant problem solving due to the intensively computational burden and low accuracy. In this paper, a unified neural approach of simultaneous identification, tracking control and disturbance rejection in the framework of the ZNN is proposed to address the time-variant tracking control of uncertain nonlinear dynamics systems (UNDS). The neural network model derived by the proposed approach captures hidden relations between inputs and outputs of the UNDS. The proposed model shows outstanding tracking performance even under the influences of uncertainties and disturbances. Then, the continuous-time model is discretized via Euler forward formula (EFF). The corresponding discrete algorithm and block diagram are also presented for the convenience of implementation. Theoretical analyses on the convergence property and discretization accuracy are presented to verify the performance of the neural network model. Finally, numerical studies, robot applications, performance comparisons and tests demonstrate the effectiveness and advantages of the proposed neural network model for the time-variant tracking control of UNDS.
published_date 2020-03-14T07:38:35Z
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score 11.0578165

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