具有隐私保护的多智能体系统自适应分布式优化控制OA

This paper investigates the distributed optimization problem for multi-agent systems(MASs)with privacy preservation.In practice,MASs often face the risk of privacy leakage.To address this,this paper proposes a state encryption function to protect the information exchanged between communicating agents and prevent potential data leakage.Adaptive control parameters are designed to dynamically adjust communication weights based on the state differences between agents,thereby accelerating the system's convergence rate.Assuming that the global objective function of the system is the sum of the local objective functions of all agents,the paper develops a gradient tracking method to estimate the average gradient sum.A distributed optimization control algorithm is proposed,enabling agents to achieve global optimization using local information without relying on global data.Through performance analysis,the proposed method is shown to effectively protect the data privacy of the MASs while ensuring that the motion trajectories of the agents rapidly converge to the optimal solution of the objective function.Finally,the effectiveness of the privacy-preserving method and the distributed control protocol is validated through simulation experiments.