Consensus-Based Distributed Optimization Enhanced by Integral Feedback

• 成果类型: Article
• 署名作者: Wang, Xuan; Mou, Shaoshuai; Anderson, Brian D. O.
• 署名单位: George Mason University; Purdue University System; Purdue University; Australian National University; Hangzhou Dianzi University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3169179
• 发表日期: 2023
• 页码: 1894-1901
• 关键词: Distributed optimization; integral feedback; multiagent networks

摘要

Inspired and underpinned by the idea of integral feedback, a distributed constant gain algorithm is proposed for multiagent networks to solve convex optimization problems with local linear constraints. Assuming agent interactions are modeled by an undirected graph, the algorithm is capable of achieving the optimum solution with an exponential convergence rate. Furthermore, inherited from the beneficial integral feedback, the proposed algorithm has attractive requirements on communication bandwidth and good robustness against disturbance. Both analytical proof and numerical simulations are provided to validate the effectiveness of the proposed distributed algorithms in solving constrained optimization problems.