{"title":"一般输出约束条件下互联非线性系统的基于动态事件的自适应 NN 输出反馈控制","authors":"Rui Meng , Changchun Hua , Kuo Li , Qidong Li","doi":"10.1016/j.neunet.2025.107452","DOIUrl":null,"url":null,"abstract":"<div><div>This paper investigates the adaptive NN output feedback tracking control problem for a class of interconnected nonlinear systems. Unlike the existing control algorithms, we propose a dynamic event-triggered output constraint control algorithm.First, a reduced-order dynamic gain K-filter is established to construct the unmeasurable state variables. Second, an asymmetric constraint function with a special time-varying function is proposed, which can handle the case where the initial values of the constraint boundaries are unlimited. Then, a dynamic event-triggered mechanism based on the arctangent function is developed, which avoids the continuous transmission of control signals. With the help of the Lyapunov stability theory, it is rigorously proved that all signals of the closed-loop systems are bounded and the tracking error satisfies the output constraint requirement.Finally, the validity of the proposed algorithm is justified by the use of a numerical simulation.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107452"},"PeriodicalIF":6.0000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output constraint\",\"authors\":\"Rui Meng , Changchun Hua , Kuo Li , Qidong Li\",\"doi\":\"10.1016/j.neunet.2025.107452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper investigates the adaptive NN output feedback tracking control problem for a class of interconnected nonlinear systems. Unlike the existing control algorithms, we propose a dynamic event-triggered output constraint control algorithm.First, a reduced-order dynamic gain K-filter is established to construct the unmeasurable state variables. Second, an asymmetric constraint function with a special time-varying function is proposed, which can handle the case where the initial values of the constraint boundaries are unlimited. Then, a dynamic event-triggered mechanism based on the arctangent function is developed, which avoids the continuous transmission of control signals. With the help of the Lyapunov stability theory, it is rigorously proved that all signals of the closed-loop systems are bounded and the tracking error satisfies the output constraint requirement.Finally, the validity of the proposed algorithm is justified by the use of a numerical simulation.</div></div>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":\"188 \",\"pages\":\"Article 107452\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893608025003314\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025003314","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output constraint
This paper investigates the adaptive NN output feedback tracking control problem for a class of interconnected nonlinear systems. Unlike the existing control algorithms, we propose a dynamic event-triggered output constraint control algorithm.First, a reduced-order dynamic gain K-filter is established to construct the unmeasurable state variables. Second, an asymmetric constraint function with a special time-varying function is proposed, which can handle the case where the initial values of the constraint boundaries are unlimited. Then, a dynamic event-triggered mechanism based on the arctangent function is developed, which avoids the continuous transmission of control signals. With the help of the Lyapunov stability theory, it is rigorously proved that all signals of the closed-loop systems are bounded and the tracking error satisfies the output constraint requirement.Finally, the validity of the proposed algorithm is justified by the use of a numerical simulation.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.