{"title":"自适应巡航控制问题的分析与模型预测相结合的优化方法","authors":"A. Tran, N. Sakamoto, Tatsuya Suzuki","doi":"10.1109/ANZCC.2018.8606602","DOIUrl":null,"url":null,"abstract":"This research develops a control design methodology that combines the analytical method and randomized model predictive control one. By using the analytical approach, one can consider the optimization in the infinite time horizon and propose several nonlinear optimal control input candidates. Then, by using the randomized model predictive control method, one can determine the \"probably true\" optimal one. The effectiveness and robustness of the proposed method are verified in simulations.","PeriodicalId":358801,"journal":{"name":"2018 Australian & New Zealand Control Conference (ANZCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A combination of analytical and model predictive optimal methods for adaptive cruise control problem\",\"authors\":\"A. Tran, N. Sakamoto, Tatsuya Suzuki\",\"doi\":\"10.1109/ANZCC.2018.8606602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research develops a control design methodology that combines the analytical method and randomized model predictive control one. By using the analytical approach, one can consider the optimization in the infinite time horizon and propose several nonlinear optimal control input candidates. Then, by using the randomized model predictive control method, one can determine the \\\"probably true\\\" optimal one. The effectiveness and robustness of the proposed method are verified in simulations.\",\"PeriodicalId\":358801,\"journal\":{\"name\":\"2018 Australian & New Zealand Control Conference (ANZCC)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Australian & New Zealand Control Conference (ANZCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANZCC.2018.8606602\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Australian & New Zealand Control Conference (ANZCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANZCC.2018.8606602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A combination of analytical and model predictive optimal methods for adaptive cruise control problem
This research develops a control design methodology that combines the analytical method and randomized model predictive control one. By using the analytical approach, one can consider the optimization in the infinite time horizon and propose several nonlinear optimal control input candidates. Then, by using the randomized model predictive control method, one can determine the "probably true" optimal one. The effectiveness and robustness of the proposed method are verified in simulations.