Distributed fixed‐time optimization for multi‐agent systems with time‐varying objective function

Abstract

Abstract In this article, a distributed time‐varying convex optimization problem for multi‐agent systems is studied. The goal of this article is to use only the local information and interaction information of each agent to drive all agents to achieve consensus and minimize the time‐varying global objective function within a fixed time. In order to solve this problem, a centralized fixed‐time algorithm based on predictive correction method is proposed, which can optimize the time‐varying global objective function based on global information within a fixed time. Then, based on the distributed average tracking method, the centralized algorithm is improved to a distributed fixed‐time algorithm based on local information. The theoretical analysis ensures that the algorithms in this article can solve the time‐varying optimization problem in a fixed time, and numerical examples are proposed to verify the effectiveness of algorithms. Finally, the distributed algorithm is applied to solve battery packages problem.