{"title":"具有执行器失效的不确定非线性多智能体系统的分布式固定时间事件触发一致性控制","authors":"Jianhui Wang, Chen Wang, Kairui Chen, Zitao Chen","doi":"10.1155/2023/8818233","DOIUrl":null,"url":null,"abstract":"A fixed-time event-triggered consensus control method is proposed for uncertain nonlinear multiagent systems with actuator failures. Since actuator failures, external disturbances and control gains are time-varying and completely unknown, the effects of these system constraints on the system are completely unknown, which makes the implementation of fixed-time tracking control challenging. To deal with these system constraints, radial basis function neural networks (RBFNNs) are applied to approximate the uncertain dynamics, and a boundary estimation method is presented to achieve adaptive compensation for them. Furthermore, considering that the implementation of this boundary estimation method requires a large number of communication resources, an event triggering mechanism is designed to reduce the update frequency of the controller. It is theoretically confirmed that using the proposed control scheme, all the followers can track the leader with sufficient accuracy in a predetermined time, and all the closed-loop signals are bounded. Finally, the simulation experiments verify the theoretical results.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2023 1","pages":"1-16"},"PeriodicalIF":5.0000,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Actuator Failures\",\"authors\":\"Jianhui Wang, Chen Wang, Kairui Chen, Zitao Chen\",\"doi\":\"10.1155/2023/8818233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A fixed-time event-triggered consensus control method is proposed for uncertain nonlinear multiagent systems with actuator failures. Since actuator failures, external disturbances and control gains are time-varying and completely unknown, the effects of these system constraints on the system are completely unknown, which makes the implementation of fixed-time tracking control challenging. To deal with these system constraints, radial basis function neural networks (RBFNNs) are applied to approximate the uncertain dynamics, and a boundary estimation method is presented to achieve adaptive compensation for them. Furthermore, considering that the implementation of this boundary estimation method requires a large number of communication resources, an event triggering mechanism is designed to reduce the update frequency of the controller. It is theoretically confirmed that using the proposed control scheme, all the followers can track the leader with sufficient accuracy in a predetermined time, and all the closed-loop signals are bounded. Finally, the simulation experiments verify the theoretical results.\",\"PeriodicalId\":14089,\"journal\":{\"name\":\"International Journal of Intelligent Systems\",\"volume\":\"2023 1\",\"pages\":\"1-16\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2023-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/8818233\",\"RegionNum\":2,\"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":"International Journal of Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/8818233","RegionNum":2,"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}
Distributed Fixed-Time Event-Triggered Consensus Control for Uncertain Nonlinear Multiagent Systems with Actuator Failures
A fixed-time event-triggered consensus control method is proposed for uncertain nonlinear multiagent systems with actuator failures. Since actuator failures, external disturbances and control gains are time-varying and completely unknown, the effects of these system constraints on the system are completely unknown, which makes the implementation of fixed-time tracking control challenging. To deal with these system constraints, radial basis function neural networks (RBFNNs) are applied to approximate the uncertain dynamics, and a boundary estimation method is presented to achieve adaptive compensation for them. Furthermore, considering that the implementation of this boundary estimation method requires a large number of communication resources, an event triggering mechanism is designed to reduce the update frequency of the controller. It is theoretically confirmed that using the proposed control scheme, all the followers can track the leader with sufficient accuracy in a predetermined time, and all the closed-loop signals are bounded. Finally, the simulation experiments verify the theoretical results.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.