{"title":"非线性非严格反馈时滞系统的自适应神经网络控制","authors":"Yuanyuan Xu, Bing Chen","doi":"10.1080/21642583.2020.1833787","DOIUrl":null,"url":null,"abstract":"This paper focuses on adaptive neural control for a class of non-strict feedback nonlinear systems with state delays and input delay. By combining integral transformation with adaptive neural control approach, a backstepping-based adaptive neural control scheme is proposed. The suggested control schemes guarantees that the tracking error converges to a small neighbourhood of the origin, meanwhile, all the closed-loop signals remain bounded. Simulation examples are used to verify the effectiveness of the proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21642583.2020.1833787","citationCount":"4","resultStr":"{\"title\":\"Adaptive neural network control for nonlinear non-strict feedback time-delay systems\",\"authors\":\"Yuanyuan Xu, Bing Chen\",\"doi\":\"10.1080/21642583.2020.1833787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on adaptive neural control for a class of non-strict feedback nonlinear systems with state delays and input delay. By combining integral transformation with adaptive neural control approach, a backstepping-based adaptive neural control scheme is proposed. The suggested control schemes guarantees that the tracking error converges to a small neighbourhood of the origin, meanwhile, all the closed-loop signals remain bounded. Simulation examples are used to verify the effectiveness of the proposed method.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2021-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/21642583.2020.1833787\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2020.1833787\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2020.1833787","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Adaptive neural network control for nonlinear non-strict feedback time-delay systems
This paper focuses on adaptive neural control for a class of non-strict feedback nonlinear systems with state delays and input delay. By combining integral transformation with adaptive neural control approach, a backstepping-based adaptive neural control scheme is proposed. The suggested control schemes guarantees that the tracking error converges to a small neighbourhood of the origin, meanwhile, all the closed-loop signals remain bounded. Simulation examples are used to verify the effectiveness of the proposed method.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory