Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

Liu, Z., Wei, H. orcid.org/0000-0002-4704-7346, Liu, K. et al. (1 more author) (2018) Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO. IEEE Transactions on Power Electronics. ISSN 0885-8993

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Copyright, Publisher and Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Dates:
  • Published (online): 2 February 2018
  • Accepted: 23 January 2018
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 01 Feb 2018 12:52
Last Modified: 22 Feb 2018 17:03
Published Version: https://doi.org/10.1109/TPEL.2018.2801331
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/TPEL.2018.2801331

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