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

Chen, Dechao; Li, Shuai; Wu, Qing; Liao, Liefa

doi:10.1016/j.neucom.2019.11.031

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

Dechao Chen, Shuai Li

, Qing Wu, Liefa Liao

Neurocomputing, Volume: 381, Pages: 282 - 297

Swansea University Author: Shuai Li

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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
Published:	Elsevier BV 2020
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URI:	https://cronfa.swan.ac.uk/Record/cronfa52966
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first_indexed	2019-12-05T13:16:14Z
last_indexed	2020-11-07T04:10:35Z
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spelling	2020-11-06T17:20:18.4042941 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 MECH 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 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 Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-11-06T17:20:18.4042941 2019-12-05T10:38:39.5849552 Professional Services ISS - Uncategorised 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
doi_str_mv	10.1016/j.neucom.2019.11.031
publisher	Elsevier BV
college_str	Professional Services
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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	2020-03-14T04:05:40Z
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score	11.037581

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

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