基于内存的延迟二阶多代理系统加速共识

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-07-04 DOI:10.1109/TSIPN.2024.3417225

Mengya Huang;Jing-Wen Yi;Li Chai

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引用次数: 0

摘要

时间延迟在现实中尤为常见，通常会对系统性能产生负面影响。为了提高收敛速度，采用记忆项设计了具有时间延迟的二阶多代理系统（MAS）快速共识协议。通过图傅里叶变换，将共识问题转化为同步稳定性问题，并给出了达成共识的必要条件和充分条件。然后，提出了一种基于梯度下降的快速共识算法，以优化收敛速度。对于延迟较小的 MAS，分别得到了基于内存协议和无内存协议的最优控制增益和最快收敛速率的显式公式。最后，给出了数值示例来说明理论结果的正确性。

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Memory-Based Accelerated Consensus for Second-Order Multi-Agent Systems With Delay

Time delay, which is particularly common in reality, generally has a negative impact on system performance. In order to improve the convergence rate, memory term is adopt to design the fast consensus protocol for second-order multi-agent systems(MASs) with time delay. Through the graph Fourier transform, the consensus problem is transformed to a simultaneous stability problem, and the necessary and sufficient condition to reach consensus is given. Then, a fast consensus algorithm based on gradient descent is proposed to optimize the convergence rate. For MASs with small delay, the explicit formulas of the optimal control gains and the fastest convergence rate are obtained for memory-based and memoryless protocols respectively. Finally, numerical examples are given to illustrate the validity of the theoretical results.

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.