{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:47:28Z","timestamp":1767649648770,"version":"3.43.0"},"reference-count":37,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001868","name":"National Science Council","doi-asserted-by":"publisher","award":["MOST 107-2221-E-239-021"],"award-info":[{"award-number":["MOST 107-2221-E-239-021"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transactions of the Institute of Measurement and Control"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p>Because of the uncertainty\u2019s action in a linear permanent magnet synchronous motor drive system such as the external load force, the cogging force, the column friction force and the Stribeck effect force and the parameters variations, it is difficult to reach specific control performances by using the existing linear controller. To raise robustness under occurrence of parameters uncertainties and external force disturbances, the smart backstepping control system with three adaptive laws is proposed for controlling the linear permanent magnet synchronous motor drive system. In accordance with the Lyapunov function, three adaptive laws are derived to ameliorate the system\u2019s robustness. Furthermore, the smart backstepping control system using revised recurrent fuzzy neural network and revised ant colony optimization with the compensated controller is proposed to improve the control performance. The revised recurrent fuzzy neural network acts as the estimator of the uncertainty\u2019s disturbances. In addition, the compensated controller with error estimation law is proposed to compensate the minimum rebuilt error. Moreover, two learning rates of the weights in the revised recurrent fuzzy neural network are derived according to the discrete-type Lyapunov stability to assure convergence of the output tracking error and are adopted by using the revised ant colony optimization to speed-up parameter\u2019s convergence. Finally, some comparative performances are verified through some tentative upshots that the smart backstepping control system by virtue of revised recurrent fuzzy neural network and revised ant colony optimization with the compensated controller results in better control performances for the linear permanent magnet synchronous motor drive system.<\/jats:p>","DOI":"10.1177\/0142331219891353","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T06:31:31Z","timestamp":1577082691000},"page":"1388-1405","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Smart backstepping control using revised recurrent fuzzy neural network and revised ant colony optimization for linear permanent magnet synchronous motor drive system"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8052-1137","authenticated-orcid":false,"given":"Chih-Hong","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National United University"}]},{"given":"Kuo-Tsai","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National United University"}]}],"member":"179","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"e_1_3_2_2_1","volume-title":"PID Controller: Theory, Design, and Tuning","author":"Astrom KJ","year":"1995","unstructured":"Astrom KJ, Hagglund T (1995) PID Controller: Theory, Design, and Tuning. 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