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Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle

Bin Xu, Chenguang Yang, Yongping Pan

IEEE Transactions on Neural Networks and Learning Systems, Volume: 26, Issue: 10, Pages: 2563 - 2575

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1109/TNNLS.2015.2456972

Abstract

In this paper, we investigate both indirect and direct global neural control of nonlinear systems in strict-feedback form. The dynamic surface control (DSC) technique is employed together with a novel switching mechanism, and neural approximation is utilized to compensate for the effect caused by un...

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Published in: IEEE Transactions on Neural Networks and Learning Systems
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa27024
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first_indexed 2016-04-02T01:02:49Z
last_indexed 2021-10-21T02:44:29Z
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spelling 2021-10-20T10:56:04.4977372 v2 27024 2016-04-01 Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2016-04-01 EEN In this paper, we investigate both indirect and direct global neural control of nonlinear systems in strict-feedback form. The dynamic surface control (DSC) technique is employed together with a novel switching mechanism, and neural approximation is utilized to compensate for the effect caused by unknown dynamics. A robust term is integrated into the control design to pull the transient states back into the neural approximation domain when they go beyond. The proposed method ensures globally uniformly ultimately boundedness stability, in comparison to the conventional semiglobal stability achieved by most existing neural controllers. Simulation studies are performed on a hypersonic flight vehicle (HFV) model to verify the effectiveness of the proposed global neural controller. Journal Article IEEE Transactions on Neural Networks and Learning Systems 26 10 2563 2575 31 10 2015 2015-10-31 10.1109/TNNLS.2015.2456972 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2021-10-20T10:56:04.4977372 2016-04-01T17:33:21.4205802 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Bin Xu 1 Chenguang Yang 2 Yongping Pan 3
title Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
spellingShingle Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
Chenguang Yang
title_short Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
title_full Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
title_fullStr Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
title_full_unstemmed Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
title_sort Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Bin Xu
Chenguang Yang
Yongping Pan
format Journal article
container_title IEEE Transactions on Neural Networks and Learning Systems
container_volume 26
container_issue 10
container_start_page 2563
publishDate 2015
institution Swansea University
doi_str_mv 10.1109/TNNLS.2015.2456972
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 Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 0
active_str 0
description In this paper, we investigate both indirect and direct global neural control of nonlinear systems in strict-feedback form. The dynamic surface control (DSC) technique is employed together with a novel switching mechanism, and neural approximation is utilized to compensate for the effect caused by unknown dynamics. A robust term is integrated into the control design to pull the transient states back into the neural approximation domain when they go beyond. The proposed method ensures globally uniformly ultimately boundedness stability, in comparison to the conventional semiglobal stability achieved by most existing neural controllers. Simulation studies are performed on a hypersonic flight vehicle (HFV) model to verify the effectiveness of the proposed global neural controller.
published_date 2015-10-31T03:32:39Z
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score 11.0018835

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