复杂网络的能量感知可控性

Giacomo Baggio, F. Pasqualetti, S. Zampieri
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引用次数: 4

摘要

理解控制复杂网络的基本原理和局限性在自然科学、社会科学和工程科学中都是至关重要的。可控性的经典概念没有捕捉到控制动态网络所需的努力,可控性的定量测量已经被提出来纠正这个问题。本文介绍了由线性动力学控制网络的实际(即能源相关)方面的介绍性概述。首先,我们引入了一类能量感知的可控性度量,并讨论了它们的性质。然后,我们建立了这些指标的界限,这使我们能够理解网络的结构如何影响控制能量。最后,我们研究了最优选择一组控制节点以使控制工作量最小化的问题,并比较了一些简单策略的性能来近似解决这个问题。在整篇文章中,我们包括结构化和随机网络的示例来说明我们的结果。预计《控制、机器人和自主系统年度评论》第5卷的最终在线出版日期是2022年5月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-Aware Controllability of Complex Networks
Understanding the fundamental principles and limitations of controlling complex networks is of paramount importance across natural, social, and engineering sciences. The classic notion of controllability does not capture the effort needed to control dynamical networks, and quantitative measures of controllability have been proposed to remedy this problem. This article presents an introductory overview of the practical (i.e., energy-related) aspects of controlling networks governed by linear dynamics. First, we introduce a class of energy-aware controllability metrics and discuss their properties. Then, we establish bounds on these metrics, which allow us to understand how the structure of the network impacts the control energy. Finally, we examine the problem of optimally selecting a set of control nodes so as to minimize the control effort, and compare the performance of some simple strategies to approximately solve this problem. Throughout the article, we include examples of structured and random networks to illustrate our results. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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