{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:28:54Z","timestamp":1740202134858,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>The maglev suspension system or magnetic levitation system (MLS), using the electromagnetic force to float, can effectively reduce the mechanical vibration friction and wearing loss caused by contact operation. However the parameters in the mathematical model are related to the permanent magnet geometry, distance and the total mass of the platform. To achieve fast response, a fuzzy system and the parameter identification algorithm for maglev suspension system are proposed in the paper. The proposed first fuzzy model method is based on the table lookup scheme (TLS) to establish a common fuzzy system procedure. By gathering the input and output data pairs, a fuzzy system is created to replace the currently used mathematical model. On the other hand, due to the parameters are related to the hardware structure, the second gradient descent algorithm (GDA) is proposed. By using the gathered data pairs, the parameters of membership functions are updated through the gradient descent algorithm. In this paper, the fuzzy model systems based on TLS and GDA are applied to ensure the effectiveness of tracking performance by replacing the mathematical model of MLS. From the results, the chattering phenomenon is reduced and fast response can be obtained without degrading the tracking performance.<\/jats:p>","DOI":"10.3233\/978-1-61499-522-7-523","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:24Z","timestamp":1740133644000},"source":"Crossref","is-referenced-by-count":0,"title":["Fuzzy Model Control for Maglev Suspension System"],"prefix":"10.3233","author":[{"family":"Su Kuo-Ho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Pham Duy-Thanh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Tsung Tsing-Tshih","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Yang Chan-Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends on System Sciences and Engineering"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:38:37Z","timestamp":1740137917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-521-0&spage=523&doi=10.3233\/978-1-61499-522-7-523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-522-7-523","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}