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Composite adaptive locally weighted learning control for multi-constraint nonlinear systems

Tairen Sun, Yongping Pan, Chenguang Yang

Applied Soft Computing, Volume: 61, Pages: 1098 - 1104

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.1016/j.asoc.2017.09.011

Abstract

A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa35248
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first_indexed 2017-09-13T18:53:36Z
last_indexed 2018-02-09T05:26:13Z
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spelling 2017-10-06T10:26:39.8089991 v2 35248 2017-09-13 Composite adaptive locally weighted learning control for multi-constraint nonlinear systems d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-09-13 EEN A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), where a prediction error is constructed via a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results. Journal Article Applied Soft Computing 61 1098 1104 1568-4946 Barrier Lyapunov function; Neural network; control saturation; state constraint; locally weighted learning 31 12 2017 2017-12-31 10.1016/j.asoc.2017.09.011 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2017-10-06T10:26:39.8089991 2017-09-13T15:03:08.5369808 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Tairen Sun 1 Yongping Pan 2 Chenguang Yang 3 0035248-13092017150652.pdf sun2017.pdf 2017-09-13T15:06:52.5870000 Output 1248871 application/pdf Accepted Manuscript true 2018-09-12T00:00:00.0000000 false eng
title Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
spellingShingle Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
Chenguang Yang
title_short Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
title_full Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
title_fullStr Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
title_full_unstemmed Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
title_sort Composite adaptive locally weighted learning control for multi-constraint nonlinear systems
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Tairen Sun
Yongping Pan
Chenguang Yang
format Journal article
container_title Applied Soft Computing
container_volume 61
container_start_page 1098
publishDate 2017
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2017.09.011
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 1
active_str 0
description A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), where a prediction error is constructed via a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results.
published_date 2017-12-31T03:43:48Z
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score 11.016258

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