文章基本信息

标题：Aggregative feedback optimization for distributed cooperative robotics
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作者：Guido Carnevale ; Nicola Mimmo ; Giuseppe Notarstefano 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：13
页码：7-12
DOI：10.1016/j.ifacol.2022.07.227
语种：English
出版社：Elsevier
摘要：AbstractIn this paper we propose Aggregative Tracking Feedback, i.e., a novel distributed feedback optimization law that steers network systems to a steady state, which is optimal according to an aggregative optimization problem. Aggregative optimization is a recently emerged distributed optimization framework in which the agents of a network minimize the sum of local objective functions. These functions depend both on local and aggregate decision variables (e.g., the barycenter). Motivated by this problem setup, we design a distributed feedback optimization law in which each agent reconstructs information not locally available while concurrently steering the network to an optimal steady state. We perform a system theoretical analysis based on a singular perturbation approach to show that Aggregative Tracking Feedback, in case of strongly convex objective functions, steers the network with a linear convergence rate to the problem minimum. Finally, we show some numerical simulations on a multi-robot surveillance scenario to validate the effectiveness of the proposed method.
关键词：KeywordsDistributed optimizationDecentralized ControlLarge-Scale SystemsRoboticsmulti-agent systems