Work

Distributed Adaptive Time-Varying Optimization With Global Asymptotic Convergence

Year: 2024

Type: article

Abstract: 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.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2024.3492952

Authors: Liangze Jiang, Zheng-Guang Wu, Lei Wang

Institution: Zhejiang University

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G4813082996, G5611804467