State feedback control for a PM hub motor based on gray Wolf optimization algorithm

Sun, X; Hu, C; Lei, G; Guo, Y; Zhu, J

State feedback control for a PM hub motor based on gray Wolf optimization algorithm

Sun, X Hu, C Lei, G

Guo, Y

Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Power Electronics, 2020, 35, (1), pp. 1136-1146
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Sun, X
dc.contributor.author	Hu, C
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2021-03-11T01:04:15Z
dc.date.available	2021-08-12T19:01:05Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Power Electronics, 2020, 35, (1), pp. 1136-1146
dc.identifier.issn	0885-8993
dc.identifier.issn	1941-0107
dc.identifier.uri	http://hdl.handle.net/10453/147020
dc.description.abstract	© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Power Electronics
dc.relation.hasversion	Accepted Manuscript version	en
dc.relation.isbasedon	10.1109/TPEL.2019.2923726
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	State feedback control for a PM hub motor based on gray Wolf optimization algorithm
dc.type	Journal Article
utslib.citation.volume	35
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
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-11T01:04:13Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	1

Abstract:

© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response.

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http://hdl.handle.net/10453/147020