摘要
针对无向图下多智能体系统的优化问题,提出一种基于周期采样机制的分布式零梯度和优化算法,并设计一种新的动态事件触发策略。该策略中加入与历史时刻智能体状态相关的动态变量,有效降低了系统通信量;所提出的算法允许采样周期任意大,并考虑了通信延时的影响,利用Lyapunov稳定性理论推导出算法收敛的充分条件。数值仿真进一步验证了所提算法的有效性。
A distributed zero-gradient-sum optimization algorithm based on a periodic sampling mechanism is proposed to address the optimization problem of multi-agent systems under undirected graphs.A novel dynamic event-triggering strategy is designed,which incorporates dynamic variables associated with the historical states of the agents to effectively reduce the system communication overhead.Moreover,the algorithm allows for arbitrary sampling periods and takes into consideration the influence of time delay.Finally,sufficient conditions for the convergence of the algorithm are derived by utilizing Lyapunov stability theory.The effectiveness of the proposed algorithm is further demonstrated through numerical simulations.
作者
夏伦超
韦梦立
季秋桐
赵中原
XIA Lunchao;WEI Mengli;JI Qiutong;ZHAO Zhongyuan(College of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China)
出处
《山东理工大学学报（自然科学版）》
CAS
2024年第3期58-64,共7页
Journal of Shandong University of Technology:Natural Science Edition
基金
江苏省自然科学基金项目(BK20200824)。
关键词
分布式优化
多智能体系统
动态事件触发
通信时延
distributed optimization
multi-agent systems
dynamic event-triggered
time delay
