{"title":"A genetic algorithm based optimisation method for iterative learning control systems","authors":"V. Hatzikos, D. Owens","doi":"10.1109/ROMOCO.2002.1177143","DOIUrl":null,"url":null,"abstract":"In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.","PeriodicalId":213750,"journal":{"name":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third International Workshop on Robot Motion and Control, 2002. RoMoCo '02.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMOCO.2002.1177143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
In this paper genetic algorithms are proposed as a method to implement optimality based iterative learning control algorithms. The strength of the proposed method is that it can cope with nonlinearities and hard constraints in the problem definition whereas most of the existing algorithms would fail. Simulation examples show that this approach results in fast convergence for linear plants.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
