{"title":"输入饱和不确定非线性系统的自适应神经网络控制","authors":"Jing Zhou, M. Er, Yi Zhou","doi":"10.1109/ICARCV.2006.345187","DOIUrl":null,"url":null,"abstract":"In this paper, we present a new scheme to design adaptive controller for uncertain nonlinear systems in the presence of input saturation. The control design is achieved by using backstepping technique and neural network. Unlike some existing control schemes for systems with input saturation, the developed controller does not require uncertain parameters within a known compact set. Besides showing stability, transient performance is also established and can be adjusted by tuning certain design parameters","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"193 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Adaptive Neural Network Control of Uncertain Nonlinear Systems in the Presence of Input Saturation\",\"authors\":\"Jing Zhou, M. Er, Yi Zhou\",\"doi\":\"10.1109/ICARCV.2006.345187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a new scheme to design adaptive controller for uncertain nonlinear systems in the presence of input saturation. The control design is achieved by using backstepping technique and neural network. Unlike some existing control schemes for systems with input saturation, the developed controller does not require uncertain parameters within a known compact set. Besides showing stability, transient performance is also established and can be adjusted by tuning certain design parameters\",\"PeriodicalId\":415827,\"journal\":{\"name\":\"2006 9th International Conference on Control, Automation, Robotics and Vision\",\"volume\":\"193 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 9th International Conference on Control, Automation, Robotics and Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCV.2006.345187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Control, Automation, Robotics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2006.345187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Neural Network Control of Uncertain Nonlinear Systems in the Presence of Input Saturation
In this paper, we present a new scheme to design adaptive controller for uncertain nonlinear systems in the presence of input saturation. The control design is achieved by using backstepping technique and neural network. Unlike some existing control schemes for systems with input saturation, the developed controller does not require uncertain parameters within a known compact set. Besides showing stability, transient performance is also established and can be adjusted by tuning certain design parameters