摘要
Abstract
Industrial agents encompass underlying equipment,sensors,actuators,controllers,production u-nits,and scheduling systems.To address communication delays,packet losses,and limited bandwidth in indus-trial networks,an asynchronous distributed alternating direction method of multipliers(ADMM)based on neigh-bor communication is developed and integrated into a distributed model predictive control(DMPC)framework.This framework decomposes globally coupled constraints,enabling each agent to compute iterative solutions u-sing only local models and neighboring information,thereby achieving collaborative optimization under commu-nication constraints.Results demonstrate that the proposed algorithm effectively guarantees output consensus tracking and maintains closed-loop stability under non-ideal network conditions,offering a feasible solution for distributed optimal control in industrial networks.关键词
多智能体/分布式模型预测控制/工业网络/协同控制/异步优化算法/D-MPCKey words
multi-agent/distributed model predictive control/industrial network/cooperative control/a-synchronous optimization algorithm/D-MPC分类
信息技术与安全科学
