This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggreg...This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.展开更多
基金supported in part by the National Key Research and Development Program of China (Grant No. 2020YFA0714300)the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Jiangsu Provincial Key Laboratory of Networked Collective Intelligence (Grant No. BM2017002)。
文摘This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol.The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.
