{"title":"最优控制的代价是改变控制","authors":"Changjun Yu, K. Teo, T. Tay","doi":"10.1109/AUCC.2013.6697242","DOIUrl":null,"url":null,"abstract":"In this paper, we consider a class of optimal control problems where the variation of the control variable is appended as a penalty term into the cost function to reduce the fluctuation of the control. A new computational approach is developed for solving this type of problems, based on control parametrization technique used in conjunction with the time scaling transform, the constraint transcription method and a smoothing technique. This computational method is supported by rigorous convergence analysis.","PeriodicalId":177490,"journal":{"name":"2013 Australian Control Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Optimal control with a cost of changing control\",\"authors\":\"Changjun Yu, K. Teo, T. Tay\",\"doi\":\"10.1109/AUCC.2013.6697242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we consider a class of optimal control problems where the variation of the control variable is appended as a penalty term into the cost function to reduce the fluctuation of the control. A new computational approach is developed for solving this type of problems, based on control parametrization technique used in conjunction with the time scaling transform, the constraint transcription method and a smoothing technique. This computational method is supported by rigorous convergence analysis.\",\"PeriodicalId\":177490,\"journal\":{\"name\":\"2013 Australian Control Conference\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Australian Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AUCC.2013.6697242\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Australian Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AUCC.2013.6697242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we consider a class of optimal control problems where the variation of the control variable is appended as a penalty term into the cost function to reduce the fluctuation of the control. A new computational approach is developed for solving this type of problems, based on control parametrization technique used in conjunction with the time scaling transform, the constraint transcription method and a smoothing technique. This computational method is supported by rigorous convergence analysis.