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

• 作者: Mohammad Saeed Sarafraz Mohammad Saleh Tavazoei
• 来源期刊: 自动化学报（英文版）
• 年，卷（期）: 2021,（9）
• 所属期刊栏目: PAPERS
• 页码范围: 1539-1548
• 页数: 10页
• 语种: 英文
• DOI: 10.1109/JAS.2021.1004111

摘要

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.