{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:03:32Z","timestamp":1774976612091,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2016EEM13"],"award-info":[{"award-number":["ZR2016EEM13"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Permanent Magnet Synchronous Motors (PMSMs) have been widely applied across various electrical systems due to their significant advantages, including high power density, high-efficiency conversion, and easy controllability. However, the issue of \u2018parameter asymmetry\u2019 (a mismatch between the controller\u2019s preset parameters and the actual system parameters) in PMSMs can lead to performance problems, such as delayed speed response and increased overshoot. The destruction of symmetry, including the asymmetric weight distribution between new and old data in the moment-of-inertia identification algorithm and the asymmetry between \u201cmeasured values and true values\u201d caused by sampling delay, is the core factor limiting the system\u2019s control performance. All these factors significantly affect the accuracy of parameter identification and the system\u2019s stability. To address this, this study focuses on the mechanical parameter identification of PMSMs with the core goal of \u201csymmetric matching between set values and true values\u201d. Firstly, a current-speed dual closed-loop vector control system model is constructed. The PI parameters are tuned to meet the symmetric tracking requirements of \u201cset value-feedback\u201d in the dual loops, and the influence of the PMSM\u2019s moment of inertia on the loop symmetry is analyzed. Secondly, the symmetry defects of traditional algorithms are highlighted, such as the imbalance between \u201cdata weight and working condition characteristics\u201d in the least-squares method and the mismatch between \u201cset inertia and true inertia\u201d caused by data saturation. Finally, a Forgetting Factor Recursive Least Squares (FFRLS) scheme is proposed: the timing asymmetry of signals is corrected via a first-order inertial link, a forgetting factor \u03bb is introduced to balance data weights, and a recursive structure is adopted to avoid data saturation. Simulation results show that when \u03bb = 0.92, the identification accuracy reaches +5% with a convergence time of 0.39 s. Moreover, dynamic symmetry can still be maintained under multiple multiples of inertia, thereby improving identification performance and ensuring symmetry in servo control.<\/jats:p>","DOI":"10.3390\/sym17111929","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T08:57:41Z","timestamp":1762851461000},"page":"1929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mechanical Parameter Identification of Permanent Magnet Synchronous Motor Based on Symmetry"],"prefix":"10.3390","volume":"17","author":[{"given":"Xing","family":"Ming","sequence":"first","affiliation":[{"name":"School of Intelligent Manufacturing, Hengxing University, Qingdao 266100, China"}]},{"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Fucong","family":"Liu","sequence":"additional","affiliation":[{"name":"CRRC School, Qingdao Technicians College, Qingdao 266229, China"}]},{"given":"Yi","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Hengxing University, Qingdao 266100, China"}]},{"given":"Bingyin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Hengxing University, Qingdao 266100, China"}]},{"given":"Shuolin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shandong University, Jinan 250061, China"}]},{"given":"Ping","family":"Yu","sequence":"additional","affiliation":[{"name":"Shandong Electrical Engineering & Equipment Group Smart Energy Engineering Co., Ltd., No. 3, Jiyi Xichang Road, Jinan 250000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, J., Gao, K., Wang, J., Bu, S., Liu, B., and Xing, J. 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