{"title":"Sliding mode-based learning control for positioning of flying pickup head","authors":"T.S. Liu, W.C. Wu","doi":"10.1109/CCA.2001.973889","DOIUrl":null,"url":null,"abstract":"To deal with repetitive runout and disturbance in near-field optical disk drives, this study develops a sliding mode based learning controller. It incorporates characteristics of sliding mode control into learning control. The reason for using sliding mode control is attributed to robust properties dealing with model uncertainty and disturbances. The learning algorithm utilizes shape functions to approximate influence functions in integral transforms and estimate the control input to perform seeking movement. It learns at each sampling instant the desired control input without prior knowledge of system dynamics. Track-seeking experiments were performed to validate the proposed method.","PeriodicalId":365390,"journal":{"name":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2001.973889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
To deal with repetitive runout and disturbance in near-field optical disk drives, this study develops a sliding mode based learning controller. It incorporates characteristics of sliding mode control into learning control. The reason for using sliding mode control is attributed to robust properties dealing with model uncertainty and disturbances. The learning algorithm utilizes shape functions to approximate influence functions in integral transforms and estimate the control input to perform seeking movement. It learns at each sampling instant the desired control input without prior knowledge of system dynamics. Track-seeking experiments were performed to validate the proposed method.