Distributed Adaptive Time-Varying Optimization With Global Asymptotic Convergence

• 成果类型: Article
• 署名作者: Jiang, Liangze; Wu, Zheng-Guang; Wang, Lei
• 署名单位: Zhejiang University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3492952
• 发表日期: 2025
• 页码: 2667-2674
• 关键词: Cost function; CONVERGENCE; Power system dynamics; Heuristic algorithms; Upper bound; COSTS; Eigenvalues and eigenfunctions; Distributed algorithms; vectors; Time-varying systems; Adaptive gain; distributed optimization; global asymptotic convergence; time-varying cost functions

摘要

In this note, we study distributed time-varying optimization for a multiagent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the introduction of the estimator relaxes the requirement for the Hessians of cost functions, and the integrated design eliminates the waiting time required in the relevant literature for estimating the global parameter during algorithm implementation. We then extend this result to a more general class of time-varying cost functions. Two examples are used to verify the proposed designs.