具有事件触发机制的抛物线 PDE 网络的自适应神经控制

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-15 DOI:10.1109/TPDS.2024.3401164

Sai Zhang;Li Tang;Yan-Jun Liu

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引用次数: 0

摘要

本文通过设计一种新的跟踪控制器，研究了非线性抛物线网络的有限时间共识问题。对于无向拓扑，新设计的控制器可以通过调整参数 $\beta (0< \beta < 1 )$ 来优化共识时间。首先，利用神经网络的近似特性来抵消代理的不确定非线性动力学，并设计了事件触发机制来节省能量和减少通信负担。其次，在事件触发机制的基础上提出了一种跟踪控制协议，驱动多代理系统在有限时间内达成领导者-跟随者共识。然后，通过考虑适当的 Lyapunov 泛化函数和使用一些重要的不等式，得到了在多代理系统中实现有限时间共识的充分条件。最后，通过仿真验证了所提出方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

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.