{"title":"具有事件触发机制的抛物线 PDE 网络的自适应神经控制","authors":"Sai Zhang;Li Tang;Yan-Jun Liu","doi":"10.1109/TPDS.2024.3401164","DOIUrl":null,"url":null,"abstract":"This paper investigates the finite-time consensus problem for nonlinear parabolic networks by designing a new tracking controller. For undirected topology, the newly designed controller allows to optimize the consensus time by adjusting the parameter \n<inline-formula><tex-math>$\\beta (0< \\beta < 1 )$</tex-math></inline-formula>\n. First, the neural network approximation property is utilized to counteract the uncertain nonlinear dynamics of agents, and the event-triggered mechanism is designed to save energy and reduce the communication burden. Second, a tracking control protocol is proposed based on event-triggered mechanism, which drives the multi-agent system to reach leader-follower consensus in finite time. Then, by considering appropriate Lyapunov generalization functions and using some important inequalities, the sufficient condition for achieving finite-time consensus in the multi-agent system is obtained. Finally, the effectiveness of the presented method is verified by simulation.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Neural Control for a Network of Parabolic PDEs With Event-Triggered Mechanism\",\"authors\":\"Sai Zhang;Li Tang;Yan-Jun Liu\",\"doi\":\"10.1109/TPDS.2024.3401164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the finite-time consensus problem for nonlinear parabolic networks by designing a new tracking controller. For undirected topology, the newly designed controller allows to optimize the consensus time by adjusting the parameter \\n<inline-formula><tex-math>$\\\\beta (0< \\\\beta < 1 )$</tex-math></inline-formula>\\n. First, the neural network approximation property is utilized to counteract the uncertain nonlinear dynamics of agents, and the event-triggered mechanism is designed to save energy and reduce the communication burden. Second, a tracking control protocol is proposed based on event-triggered mechanism, which drives the multi-agent system to reach leader-follower consensus in finite time. Then, by considering appropriate Lyapunov generalization functions and using some important inequalities, the sufficient condition for achieving finite-time consensus in the multi-agent system is obtained. Finally, the effectiveness of the presented method is verified by simulation.\",\"PeriodicalId\":13257,\"journal\":{\"name\":\"IEEE Transactions on Parallel and Distributed Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Parallel and Distributed Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10531050/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10531050/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
Adaptive Neural Control for a Network of Parabolic PDEs With Event-Triggered Mechanism
This paper investigates the finite-time consensus problem for nonlinear parabolic networks by designing a new tracking controller. For undirected topology, the newly designed controller allows to optimize the consensus time by adjusting the parameter
$\beta (0< \beta < 1 )$
. First, the neural network approximation property is utilized to counteract the uncertain nonlinear dynamics of agents, and the event-triggered mechanism is designed to save energy and reduce the communication burden. Second, a tracking control protocol is proposed based on event-triggered mechanism, which drives the multi-agent system to reach leader-follower consensus in finite time. Then, by considering appropriate Lyapunov generalization functions and using some important inequalities, the sufficient condition for achieving finite-time consensus in the multi-agent system is obtained. Finally, the effectiveness of the presented method is verified by simulation.
期刊介绍：
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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