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Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective

Zhan Li, Shuai Li Orcid Logo

IEEE Transactions on Circuits and Systems II: Express Briefs, Volume: 68, Issue: 2, Pages: 742 - 746

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tcsii.2020.3007879

Abstract

The saturated PI (proportional-integral) based method is widely applied in nonlinear system control fields. It can be regarded as a black-box type approach with the saturated control input and utilizes the tracking error of the system output with its integral information. However, as precise descrip...

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Published in: IEEE Transactions on Circuits and Systems II: Express Briefs
ISSN: 1549-7747 1558-3791
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56220
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last_indexed 2021-03-03T04:21:20Z
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spelling 2021-03-02T16:31:54.8053929 v2 56220 2021-02-09 Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-02-09 MECH The saturated PI (proportional-integral) based method is widely applied in nonlinear system control fields. It can be regarded as a black-box type approach with the saturated control input and utilizes the tracking error of the system output with its integral information. However, as precise description of plants’ system details is usually difficult, saturated PI control approaches have to empirically tune the proportional and integral parameters to guarantee reliable convergence, making its general convergence mechanism not interpreted. In this brief, for the first time, the convergence of the saturated PI control scheme is proved through an optimization solver based on a primal dual neural network. Illustrate examples including control of an inverted-pendulum mobile vehicle and a manipulator demonstrate the efficiency of the proposed saturated PI control method in such an optimization perspective. Journal Article IEEE Transactions on Circuits and Systems II: Express Briefs 68 2 742 746 Institute of Electrical and Electronics Engineers (IEEE) 1549-7747 1558-3791 1 2 2021 2021-02-01 10.1109/tcsii.2020.3007879 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2021-03-02T16:31:54.8053929 2021-02-09T11:01:26.3211232 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Zhan Li 1 Shuai Li 0000-0001-8316-5289 2 56220__19252__9c76755cafe542a58d7766a18e05d85e.pdf 56220.pdf 2021-02-09T11:43:21.4936415 Output 761050 application/pdf Accepted Manuscript true true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
spellingShingle Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
Shuai Li
title_short Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
title_full Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
title_fullStr Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
title_full_unstemmed Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
title_sort Saturated PI Control for Nonlinear System With Provable Convergence: An Optimization Perspective
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Zhan Li
Shuai Li
format Journal article
container_title IEEE Transactions on Circuits and Systems II: Express Briefs
container_volume 68
container_issue 2
container_start_page 742
publishDate 2021
institution Swansea University
issn 1549-7747
1558-3791
doi_str_mv 10.1109/tcsii.2020.3007879
publisher Institute of Electrical and Electronics Engineers (IEEE)
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 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
document_store_str 1
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description The saturated PI (proportional-integral) based method is widely applied in nonlinear system control fields. It can be regarded as a black-box type approach with the saturated control input and utilizes the tracking error of the system output with its integral information. However, as precise description of plants’ system details is usually difficult, saturated PI control approaches have to empirically tune the proportional and integral parameters to guarantee reliable convergence, making its general convergence mechanism not interpreted. In this brief, for the first time, the convergence of the saturated PI control scheme is proved through an optimization solver based on a primal dual neural network. Illustrate examples including control of an inverted-pendulum mobile vehicle and a manipulator demonstrate the efficiency of the proposed saturated PI control method in such an optimization perspective.
published_date 2021-02-01T04:11:01Z
_version_ 1763753759834374144
score 11.013148

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