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Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach

Dechao Chen Orcid Logo, Lin Zhuo, Yifan Shao, Shuai Li Orcid Logo, Shuai Li Orcid Logo, Christian Griffiths, Ashraf Fahmy Abdo Orcid Logo

IEEE Transactions on Automation Science and Engineering, Volume: 21, Issue: 4, Pages: 5344 - 5356

Swansea University Authors: Shuai Li Orcid Logo, Christian Griffiths, Ashraf Fahmy Abdo Orcid Logo

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DOI (Published version): 10.1109/tase.2023.3310498

Abstract

The immediate feedback tracking control system design of heterogeneous robots with uncertainty is considered to be a significant issue in robotic research. Note that when the robot information is uncertain, the scale of computation would become increasingly large and the accuracy of tracking control...

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Published in: IEEE Transactions on Automation Science and Engineering
ISSN: 1545-5955 1558-3783
Published: Institute of Electrical and Electronics Engineers (IEEE) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa64418
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Note that when the robot information is uncertain, the scale of computation would become increasingly large and the accuracy of tracking control would become exceptionally low. The realization of the immediate feedback control system of heterogeneous robots with uncertainty remains to be a challenging problem. Many conventional zeroing neural network (CZNN) models have been developed accordingly. However, most of them are supported by the hypothesis that the robot parameters are complete and accurate, and the associated models possess the exponential convergence property. To handle the robot uncertainty as well as to improve the convergence performance, a new zeroing neural network (ZNN) with super-exponential convergence (SEC) rate is put forward in this paper termed SEC-ZNN, to resolve the robust control issue of uncertain heterogeneous robots. The proposed SEC-ZNN takes full advantage of effector real-time information, with robust controlling and super-exponential convergence performance so far as to the robot information is uncertain. Theoretically, the super-exponential convergence properties including lower error bound and faster convergence rate are rigorously proved. Moreover, circular path-tracking example, comparisons and tests via MATLAB, Coppeliasim and experiment via robot INNFOS substantiate the efficaciousness and preponderance of the SEC-ZNN for the immediate feedback control system for heterogeneous robots with uncertainty. Note to Practitioners &#x2014;This paper is motivated by the problem that most robots which need real-time tracking control in real applications come with uncertainty. It is important to note that traditional robot tracking control algorithms mostly require complete robot information or assume information complete, which does not correspond to the actual situation of robot control. Moreover, for practical applications in robotics, the real-time tracking control problem is very attractive. Therefore, an accurate, efficient and stable solution is of great significance to practitioners in this area. In this paper, the SEC-ZNN algorithm is proposed to solve the problem of real-time control of heterogeneous robots with uncertainty in real applications for practitioners. The proposed methos makes full use of the real-time feedback infromation to solve the real-time tracking control problem of heterogeneous robots with uncertainty at the velocity level. The algorithmic steps and principle explanation of the SEC-ZNN scheme are also presented for better understanding. Simulation studies and comparisons are performed on a Stewart robot to confirm the effectiveness and superiority of the proposed scheme. Furthermore, the simulation experiment in Coppeliasim platform is performed to confirm the possibility of portability of the SEC-ZNN to real robot operations. 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spelling 2024-11-26T16:48:37.6345814 v2 64418 2023-09-05 Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 84c202c256a2950fbc52314df6ec4914 Christian Griffiths Christian Griffiths true false b952b837f8a8447055210d209892b427 0000-0003-1624-1725 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 2023-09-05 ACEM The immediate feedback tracking control system design of heterogeneous robots with uncertainty is considered to be a significant issue in robotic research. Note that when the robot information is uncertain, the scale of computation would become increasingly large and the accuracy of tracking control would become exceptionally low. The realization of the immediate feedback control system of heterogeneous robots with uncertainty remains to be a challenging problem. Many conventional zeroing neural network (CZNN) models have been developed accordingly. However, most of them are supported by the hypothesis that the robot parameters are complete and accurate, and the associated models possess the exponential convergence property. To handle the robot uncertainty as well as to improve the convergence performance, a new zeroing neural network (ZNN) with super-exponential convergence (SEC) rate is put forward in this paper termed SEC-ZNN, to resolve the robust control issue of uncertain heterogeneous robots. The proposed SEC-ZNN takes full advantage of effector real-time information, with robust controlling and super-exponential convergence performance so far as to the robot information is uncertain. Theoretically, the super-exponential convergence properties including lower error bound and faster convergence rate are rigorously proved. Moreover, circular path-tracking example, comparisons and tests via MATLAB, Coppeliasim and experiment via robot INNFOS substantiate the efficaciousness and preponderance of the SEC-ZNN for the immediate feedback control system for heterogeneous robots with uncertainty. Note to Practitioners —This paper is motivated by the problem that most robots which need real-time tracking control in real applications come with uncertainty. It is important to note that traditional robot tracking control algorithms mostly require complete robot information or assume information complete, which does not correspond to the actual situation of robot control. Moreover, for practical applications in robotics, the real-time tracking control problem is very attractive. Therefore, an accurate, efficient and stable solution is of great significance to practitioners in this area. In this paper, the SEC-ZNN algorithm is proposed to solve the problem of real-time control of heterogeneous robots with uncertainty in real applications for practitioners. The proposed methos makes full use of the real-time feedback infromation to solve the real-time tracking control problem of heterogeneous robots with uncertainty at the velocity level. The algorithmic steps and principle explanation of the SEC-ZNN scheme are also presented for better understanding. Simulation studies and comparisons are performed on a Stewart robot to confirm the effectiveness and superiority of the proposed scheme. Furthermore, the simulation experiment in Coppeliasim platform is performed to confirm the possibility of portability of the SEC-ZNN to real robot operations. Finally, applications on a real-world robot INNFOS verify the physical relizability of the proposed SEC-ZNN for the engineering practice via heterogeneous robots. Journal Article IEEE Transactions on Automation Science and Engineering 21 4 5344 5356 Institute of Electrical and Electronics Engineers (IEEE) 1545-5955 1558-3783 Zeroing neural network, super-exponential convergence, heterogeneous robots, uncertainty, robust tracking control 1 10 2024 2024-10-01 10.1109/tase.2023.3310498 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Not Required 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62276085 and 61906054) Natural Science Foundation of Zhejiang Provincial (Grant Number: LY21F030006) 2024-11-26T16:48:37.6345814 2023-09-05T08:24:19.9765623 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Dechao Chen 0000-0002-5171-1414 1 Lin Zhuo 2 Yifan Shao 3 Shuai Li 0000-0001-8316-5289 4 Shuai Li 0000-0002-9574-9609 5 Christian Griffiths 6 Ashraf Fahmy Abdo 0000-0003-1624-1725 7 64418__28790__07260702bb4c410cb39aed947a8e529d.pdf 64418.AAM.pdf 2023-10-16T10:04:47.9621823 Output 2514416 application/pdf Accepted Manuscript true Accepted manuscript version. true eng
title Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
spellingShingle Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
Shuai Li
Christian Griffiths
Ashraf Fahmy Abdo
title_short Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
title_full Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
title_fullStr Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
title_full_unstemmed Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
title_sort Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
84c202c256a2950fbc52314df6ec4914
b952b837f8a8447055210d209892b427
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
84c202c256a2950fbc52314df6ec4914_***_Christian Griffiths
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
author Shuai Li
Christian Griffiths
Ashraf Fahmy Abdo
author2 Dechao Chen
Lin Zhuo
Yifan Shao
Shuai Li
Shuai Li
Christian Griffiths
Ashraf Fahmy Abdo
format Journal article
container_title IEEE Transactions on Automation Science and Engineering
container_volume 21
container_issue 4
container_start_page 5344
publishDate 2024
institution Swansea University
issn 1545-5955
1558-3783
doi_str_mv 10.1109/tase.2023.3310498
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
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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
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description The immediate feedback tracking control system design of heterogeneous robots with uncertainty is considered to be a significant issue in robotic research. Note that when the robot information is uncertain, the scale of computation would become increasingly large and the accuracy of tracking control would become exceptionally low. The realization of the immediate feedback control system of heterogeneous robots with uncertainty remains to be a challenging problem. Many conventional zeroing neural network (CZNN) models have been developed accordingly. However, most of them are supported by the hypothesis that the robot parameters are complete and accurate, and the associated models possess the exponential convergence property. To handle the robot uncertainty as well as to improve the convergence performance, a new zeroing neural network (ZNN) with super-exponential convergence (SEC) rate is put forward in this paper termed SEC-ZNN, to resolve the robust control issue of uncertain heterogeneous robots. The proposed SEC-ZNN takes full advantage of effector real-time information, with robust controlling and super-exponential convergence performance so far as to the robot information is uncertain. Theoretically, the super-exponential convergence properties including lower error bound and faster convergence rate are rigorously proved. Moreover, circular path-tracking example, comparisons and tests via MATLAB, Coppeliasim and experiment via robot INNFOS substantiate the efficaciousness and preponderance of the SEC-ZNN for the immediate feedback control system for heterogeneous robots with uncertainty. Note to Practitioners —This paper is motivated by the problem that most robots which need real-time tracking control in real applications come with uncertainty. It is important to note that traditional robot tracking control algorithms mostly require complete robot information or assume information complete, which does not correspond to the actual situation of robot control. Moreover, for practical applications in robotics, the real-time tracking control problem is very attractive. Therefore, an accurate, efficient and stable solution is of great significance to practitioners in this area. In this paper, the SEC-ZNN algorithm is proposed to solve the problem of real-time control of heterogeneous robots with uncertainty in real applications for practitioners. The proposed methos makes full use of the real-time feedback infromation to solve the real-time tracking control problem of heterogeneous robots with uncertainty at the velocity level. The algorithmic steps and principle explanation of the SEC-ZNN scheme are also presented for better understanding. Simulation studies and comparisons are performed on a Stewart robot to confirm the effectiveness and superiority of the proposed scheme. Furthermore, the simulation experiment in Coppeliasim platform is performed to confirm the possibility of portability of the SEC-ZNN to real robot operations. Finally, applications on a real-world robot INNFOS verify the physical relizability of the proposed SEC-ZNN for the engineering practice via heterogeneous robots.
published_date 2024-10-01T08:24:23Z
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score 11.04748

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