在随机环境中近似共识多智能体控制及其在负载平衡中的应用
Approximate Consensus Multi-Agent Control Under Stochastic Environment with Application to Load Balancing
AI总结：
本文提出一种非消失步长的局部投票协议，用于非线性智能体网络在切换拓扑、噪声和延迟测量下的近似共识问题，应用于具有不完全信息的动态网络负载平衡。
摘要：
本文致力于研究非线性智能体网络在切换拓扑、噪声和延迟测量下的近似共识问题。与现有基于随机逼近的控制算法不同，本文提出了一种具有非消失步长的局部投票协议。非消失（例如常数）步长协议可通过选择合适的步长实现更好的收敛速度，以应对时间变化的负载和智能体状态。代价是将均方收敛替换为近似收敛。为了分析闭环系统的动态，采用了所谓的平均模型方法，该方法可以降低闭环系统分析的复杂性。本文提出了均方距离上界，用于获得近似共识的条件。该方法应用于具有不完全信息的动态网络负载平衡问题，其中通信链接集发生变化。负载平衡问题被建模为具有切换拓扑的噪声模型下的共识问题。获得了实现最优负载平衡的条件（即如果没有新任务到达，所有智能体将同时完成）。系统性能通过分析和仿真评估，结果表明，具有任务在“连接”邻居间重新分配的自适应多智能体策略性能显著优于无任务重新分配的策略。获得的结果对生产网络、多处理器、传感器或多计算机网络的控制具有重要意义。
英文摘要：
The paper is devoted to the approximate consensus problem for networks of nonlinear agents with switching topology, noisy and delayed measurements. In contrast to the existing stochastic approximation-based control algorithms (protocols), a local voting protocol with nonvanishing step size is proposed. Nonvanishing (e.g., constant) step size protocols give the opportunity to achieve better convergence rate (by choosing proper step sizes) in coping with time-varying loads and agent states. The price to pay is replacement of the mean square convergence with an approximate one. To analyze dynamics of the closed loop system, the so-called method of averaged models is used. It allows to reduce analysis complexity of the closed loop system. In this paper the upper bounds for mean square distance between the initial system and its approximate averaged model are proposed. The proposed upper bounds are used to obtain conditions for approximate consensus achievement. The method is applied to the load balancing problem in stochastic dynamic networks with incomplete information about the current states of agents and with changing set of communication links. The load balancing problem is formulated as consensus problem in noisy model with switched topology. The conditions to achieve the optimal level of load balancing (in the sense that if no new task arrives, all agents will finish at the same time) are obtained. The performance of the system is evaluated analytically and by simulation. It is shown that the performance of the adaptive multi-agent strategy with the redistribution of tasks among "connected" neighbors is significantly better than the performance without redistribution. The obtained results are important for control of production networks, multiprocessor, sensor or multicomputer networks, etc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
