{"title":"履带式机器人的神经网络控制系统","authors":"T. Kuzmina, G. Dubrovskiy","doi":"10.1109/EICONRUSNW.2015.7102268","DOIUrl":null,"url":null,"abstract":"In this paper the designing of a tracked robot's neural network control system is considered. The control system embodies a black line following algorithm, which is using two infrared reflector sensors for black line recognition. The neural network regulator is designed in Matlab/Simulink using the Real-Time Windows Target Toolbox. With the purpose of the neural network regulator training, course passage results of the robot with a fuzzy regulator are used.","PeriodicalId":268759,"journal":{"name":"2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural network control system for a tracked robot\",\"authors\":\"T. Kuzmina, G. Dubrovskiy\",\"doi\":\"10.1109/EICONRUSNW.2015.7102268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper the designing of a tracked robot's neural network control system is considered. The control system embodies a black line following algorithm, which is using two infrared reflector sensors for black line recognition. The neural network regulator is designed in Matlab/Simulink using the Real-Time Windows Target Toolbox. With the purpose of the neural network regulator training, course passage results of the robot with a fuzzy regulator are used.\",\"PeriodicalId\":268759,\"journal\":{\"name\":\"2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EICONRUSNW.2015.7102268\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EICONRUSNW.2015.7102268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper the designing of a tracked robot's neural network control system is considered. The control system embodies a black line following algorithm, which is using two infrared reflector sensors for black line recognition. The neural network regulator is designed in Matlab/Simulink using the Real-Time Windows Target Toolbox. With the purpose of the neural network regulator training, course passage results of the robot with a fuzzy regulator are used.
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