Sun, T. orcid.org/0000-0002-5518-0315, Wang, J. orcid.org/0000-0003-4870-3744, Koc, M. orcid.org/0000-0003-1465-1878 et al. (1 more author) (2016) Self-Learning MTPA Control of Interior Permanent-Magnet Synchronous Machine Drives Based on Virtual Signal Injection. IEEE Transactions on Industry Applications, 52 (4). pp. 3062-3070. ISSN 0093-9994
Abstract
This paper describes a simple but effective novel self-learning maximum torque per ampere (MTPA) control scheme for interior permanent-magnet synchronous machine (IPMSM) drives to achieve fast dynamic response in tracking the MTPA points without accurate prior knowledge of machine parameters. The proposed self-learning control (SLC) scheme generates the optimal d-axis current command for MTPA operation after training. Virtual signal injection control (VSIC), which has been recently developed as a novel parameter-independent MTPA points tracking scheme, is utilized to train the SLC and compensate the error of the SLC during its operation. In this way, the proposed SLC can achieve the MTPA operation accurately with fast response and the online training of the SLC will not affect MTPA operation of IPMSM drives. The proposed control scheme is verified by simulations and experiments under various operation conditions on a prototype IPMSM drive system.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 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. |
Keywords: | Torque; Training; Fuzzy logic; Table lookup; Harmonic analysis; Permanent magnet machines |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Oct 2016 15:24 |
Last Modified: | 21 Mar 2018 15:50 |
Published Version: | http://dx.doi.org/10.1109/TIA.2016.2533601 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TIA.2016.2533601 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:105457 |