{"title":"老虎机实验中的进化算法和强化学习","authors":"Dan Martinec, M. Bundzel","doi":"10.1109/PC.2013.6581401","DOIUrl":null,"url":null,"abstract":"Some control systems are difficult or impossible to be tuned by other means than automatically. We present here examples of optimization of the parameters of a PID controller regulating velocity of a slot car to the given set point using evolutionary optimization and reinforcement learning. These methods are implemented on the micro-controller of the slot car. Experimental results and comparison are provided.","PeriodicalId":232418,"journal":{"name":"2013 International Conference on Process Control (PC)","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Evolutionary algorithms and reinforcement learning in experiments with slot cars\",\"authors\":\"Dan Martinec, M. Bundzel\",\"doi\":\"10.1109/PC.2013.6581401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Some control systems are difficult or impossible to be tuned by other means than automatically. We present here examples of optimization of the parameters of a PID controller regulating velocity of a slot car to the given set point using evolutionary optimization and reinforcement learning. These methods are implemented on the micro-controller of the slot car. Experimental results and comparison are provided.\",\"PeriodicalId\":232418,\"journal\":{\"name\":\"2013 International Conference on Process Control (PC)\",\"volume\":\"24 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Process Control (PC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PC.2013.6581401\",\"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 International Conference on Process Control (PC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PC.2013.6581401","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolutionary algorithms and reinforcement learning in experiments with slot cars
Some control systems are difficult or impossible to be tuned by other means than automatically. We present here examples of optimization of the parameters of a PID controller regulating velocity of a slot car to the given set point using evolutionary optimization and reinforcement learning. These methods are implemented on the micro-controller of the slot car. Experimental results and comparison are provided.
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