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The input of traditional feedback control is used as the stabilization control part, while the feed-forward is incorporated into the intelligent part to compensate for the uncertainties of repetitive load torque and model parameters. The proposed feed forward compensation term uses simple learning rules without any load torque disturbance observer. The additional learning feed forward term does not require information about motor parameters and load torque values, it is insensitive to load torque uncertainty and model parameters, and does not need to identify the system model. With that, the solidness and intermingling confirmation of the proposed control framework reaction is given. The exploratory outcomes demonstrate that the proposed technique has littler speed overshoot list, and the heap torque against aggravation capacity list is improved by over 30%.<\/jats:p>","DOI":"10.3233\/jifs-179930","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T13:42:32Z","timestamp":1593783752000},"page":"1573-1579","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Permanent magnet synchronous motor algorithms based on nonlinear identification generalized predictive and Intelligent fuzzy control system"],"prefix":"10.1177","volume":"39","author":[{"given":"Cao","family":"Yonglei","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing, China"}]},{"given":"Zhang","family":"Xiaodong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2020,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2013.07.008"},{"key":"e_1_3_1_3_2","unstructured":"ZhangY. 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