Shlimet, S.B. orcid.org/0009-0001-2084-6180 and Griffo, A. orcid.org/0000-0001-5642-2921 (2025) Model predictive MRAS speed estimator for permanent magnet synchronous machines. The Journal of Engineering, 2025 (1). e70141. ISSN: 2051-3305
Abstract
This paper presents a predictive model reference adaptive system (MRAS) speed estimator for permanent magnet synchronous machines. The speed estimator is based on the finite control set model predictive control principle. A search method is utilized to find the best estimated speed at each sampling interval that produces the smallest speed estimation error signal. This method replaces the PI controller which is typically employed by MRAS estimators. The speed estimator has been experimentally tested using a 2.1 kW PMSM. Results show improved performance, especially during fast load transients for the novel method compared to the PI-based MRAS method.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2025 The Author(s). The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | AC machines; electric drives; electrical vehicle; model predictive control; model reference adaptive control systems; permanent magnet motors; predictive control; sensorless machine control |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
| Date Deposited: | 10 Nov 2025 14:40 |
| Last Modified: | 10 Nov 2025 14:40 |
| Status: | Published |
| Publisher: | Institution of Engineering and Technology (IET) |
| Refereed: | Yes |
| Identification Number: | 10.1049/tje2.70141 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:234237 |

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