返回
Distributed Continuous-Time Algorithm for Constrained Optimization of Networked Euler-Lagrange Systems
DOI:10.1109/TCNS.2021.3068352.png)
摘要
En 中文
This article studies a distributed constrained optimization problem of multiple agents characterized by Euler-Lagrange systems with unknown inertial parameters. The optimization objective is to compute a feasible solution within the intersection of a series of constrained sets such that a global payoff function summed by a group of local ones is minimized. Meanwhile, each local payoff function and constrained set are just privately available to their respective agent. To accomplish the concerned constrained optimization objective, a fully distributed continuous-time algorithm resorting to a projection-based auxiliary dynamics is synthesized without using global topology information. The proposed distributed optimization algorithm is privacy-preserving in the sense that no actual state information is exchanged between distinct agents during the seeking progress. Besides, the associated stability analysis is carried out in terms of the Lyapunov theorem. Finally, a source localization example is performed to verify the effectiveness of the proposed distributed optimization algorithm.
Keyword:
Constrained optimization
distributed algorithm
Euler-Lagrange system
projection
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5
论文数:
1.7K
被引数:
5.8K
机构
引用论文
Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach多代理系统的分布式优化: 基于边缘的固定时间共识方法
Practical fixed-time consensus for integrator-type multi-agent systems: A time base generator approach积分器型多智能体系统的实用固定时间一致性: 时基生成器方法
AUTOMATICA
IF5.9
A fixed-time convergent algorithm for distributed convex optimization in multi-agent systems多智能体系统中分布式凸优化的固定时间收敛算法
AUTOMATICA
IF5.9
Exponential convergence of distributed primal-dual convex optimization algorithm without strong convexity无强凸的分布原-对偶凸优化算法的指数收敛
AUTOMATICA
IF5.9

