Model-Free Adaptive Super-Twisting Sliding Mode Speed Control Based on RBFNN Estimator for PMLSM Drive Systems

Teng, Q; Wang, X; Xu, K

Model-Free Adaptive Super-Twisting Sliding Mode Speed Control Based on RBFNN Estimator for PMLSM Drive Systems

Teng, Q Wang, X Xu, K

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Iet Electric Power Applications, 2025, 19, (1)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Teng, Q
dc.contributor.author	Wang, X
dc.contributor.author	Xu, K https://orcid.org/0000-0002-0538-1346
dc.date.accessioned	2026-07-10T02:01:34Z
dc.date.available	2026-07-10T02:01:34Z
dc.date.issued	2025-01-01
dc.identifier.citation	Iet Electric Power Applications, 2025, 19, (1)
dc.identifier.issn	1751-8660
dc.identifier.issn	1751-8679
dc.identifier.uri	http://hdl.handle.net/10453/195605
dc.description.abstract	Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Iet Electric Power Applications
dc.relation.isbasedon	10.1049/elp2.70048
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4002 Automotive engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Model-Free Adaptive Super-Twisting Sliding Mode Speed Control Based on RBFNN Estimator for PMLSM Drive Systems
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-07-10T02:01:32Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	1

Abstract:

Considering the various unknown and uncertain parameters as well as load disturbances of permanent magnet linear synchronous motor (PMLSM) drive systems, this paper proposes a novel model-free adaptive super-twisting (MFAST) speed control strategy based on radial basis function neural network (RBFNN) estimator to ensure the satisfactory performance and strong robustness of the speed control. First, by considering all possible unknown and uncertain parameters, the ultralocal model of PMLSM is constructed. Next, the RBFNN estimator is designed to estimate the unknown parameters of the above-mentioned ultralocal model. Finally, the RBFNN-based MFAST control law is proposed to guarantee PMLSM drive systems' robustness against various internal and external disturbances. StarSim HIL experiment results demonstrate that the synthesised RBFNN-based MFAST control strategy can enable PMLSM drive systems to possess high accuracy, remarkable rapidity and strong robustness.

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http://hdl.handle.net/10453/195605