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A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control

Dechao Chen, Xinwei Cao, Shuai Li Orcid Logo

Neurocomputing, Volume: 460, Pages: 331 - 344

Swansea University Author: Shuai Li Orcid Logo

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Abstract

Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been dee different time-dependent issues. However, due to the high nonlinearity and complexity, the research on finding a feasible ZNN to address time-dependent nonlinear optimization with multiple types...

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Published in: Neurocomputing
ISSN: 0925-2312
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57250
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spelling 2021-08-04T15:27:30.3907169 v2 57250 2021-07-01 A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-07-01 MECH Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been dee different time-dependent issues. However, due to the high nonlinearity and complexity, the research on finding a feasible ZNN to address time-dependent nonlinear optimization with multiple types of constraints still remains stagnant. To simultaneously handle multiple types of constraints for the time-dependent nonlinear optimization, this paper proposes a novel neural-network based model in a unified framework of ZNN. By using leveraging the Lagrange method, the time-dependent nonlinear optimization problem with multiple types of constraints is converted to a time-dependent equality system. The proposed multi-constrained ZNN (termed MZNN) inherently possesses the effectiveness of exponential convergence property by utilizing the time-derivative information. Theoretical analyses on the global stability and exponential convergence property are rigorously provided. Then, three general numerical examples in time-independent and time-dependent cases verify the computational performance of the proposed MZNN. An application based on the mobile robot for nonlinear optimization control sufficiently demonstrates the physical effectiveness of the proposed MZNN for the control of mobile robot with both performance-index optimization and multiple physical-limit constraints. Finally, comparisons with existing neural networks such as gradient neural network (GNN), and performance tests with investigation on computational complexity substantiate the superiority of the MZNN for the time-dependent nonlinear optimization subject to multiple types of constraints. Journal Article Neurocomputing 460 331 344 Elsevier BV 0925-2312 Multi-constrained zeroing neural networks (MZNNs), Time-dependent problem, Nonlinear optimization, Multiple constraints, Robot control 2010 MSC: 62G35, 92B20, 93B51 14 10 2021 2021-10-14 10.1016/j.neucom.2021.06.089 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-08-04T15:27:30.3907169 2021-07-01T09:30:18.3969451 College of Engineering Engineering Dechao Chen 1 Xinwei Cao 2 Shuai Li 0000-0001-8316-5289 3 57250__20311__c237a58524d34f1d92cc719e3fd252eb.pdf 57250.pdf 2021-07-01T09:32:29.7451306 Output 805839 application/pdf Accepted Manuscript true 2022-06-30T00:00:00.0000000 Released under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
spellingShingle A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
Shuai Li
title_short A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
title_full A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
title_fullStr A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
title_full_unstemmed A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
title_sort A multi-constrained zeroing neural network for time-dependent nonlinear optimization with application to mobile robot tracking control
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Dechao Chen
Xinwei Cao
Shuai Li
format Journal article
container_title Neurocomputing
container_volume 460
container_start_page 331
publishDate 2021
institution Swansea University
issn 0925-2312
doi_str_mv 10.1016/j.neucom.2021.06.089
publisher Elsevier BV
college_str College of Engineering
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hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
document_store_str 1
active_str 0
description Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been dee different time-dependent issues. However, due to the high nonlinearity and complexity, the research on finding a feasible ZNN to address time-dependent nonlinear optimization with multiple types of constraints still remains stagnant. To simultaneously handle multiple types of constraints for the time-dependent nonlinear optimization, this paper proposes a novel neural-network based model in a unified framework of ZNN. By using leveraging the Lagrange method, the time-dependent nonlinear optimization problem with multiple types of constraints is converted to a time-dependent equality system. The proposed multi-constrained ZNN (termed MZNN) inherently possesses the effectiveness of exponential convergence property by utilizing the time-derivative information. Theoretical analyses on the global stability and exponential convergence property are rigorously provided. Then, three general numerical examples in time-independent and time-dependent cases verify the computational performance of the proposed MZNN. An application based on the mobile robot for nonlinear optimization control sufficiently demonstrates the physical effectiveness of the proposed MZNN for the control of mobile robot with both performance-index optimization and multiple physical-limit constraints. Finally, comparisons with existing neural networks such as gradient neural network (GNN), and performance tests with investigation on computational complexity substantiate the superiority of the MZNN for the time-dependent nonlinear optimization subject to multiple types of constraints.
published_date 2021-10-14T04:13:17Z
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score 10.9201975