A Unified Optimization-Based Framework to Adjust Consensus Convergence Rate and Optimize the Network Topology in Uncertain Multi-Agent Systems

Autor:	Mohammad Saleh Tavazoei, Mohammad Saeed Sarafraz
Rok vydání:	2021
Předmět:	Mathematical optimization Optimization problem Rate of convergence Consensus Artificial Intelligence Control and Systems Engineering Computer science Distributed algorithm Multi-agent system Convergence (routing) Graph (abstract data type) Network topology Information Systems
Zdroj:	IEEE/CAA Journal of Automatica Sinica. 8:1539-1548
ISSN:	2329-9274 2329-9266
DOI:	10.1109/jas.2021.1004111
Popis:	This paper deals with the consensus problem in an uncertain multi-agent system whose agents communicate with each other through a weighted undirected (primary) graph. The considered multi-agent system is described by an uncertain state-space model in which the involved matrices belong to some matrix boxes. As the main contribution of the paper, a unified optimization-based framework is proposed for simultaneously reducing the weights of the edges of the primary communication graph (optimizing the network topology) and synthesizing a controller such that the consensus in the considered uncertain multi-agent system is ensured with an adjustable convergence rate. Considering the NP-hardness nature of the optimization problem related to the aforementioned framework, this problem is relaxed such that it can be solved by regular LMI solvers. Numerical/practical-based examples are presented to verify the usefulness of the obtained results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::381e86d2420cd7aa0fd05eed5837d887 https://doi.org/10.1109/jas.2021.1004111 Zobrazit plný text záznamu