Distributed Estimation of Stochastic Multiagent Systems for Cooperative Control With a Virtual Network
提出一种仅依赖局部感知信息的分布式估计算法，通过建立虚拟全连接网络实现非邻居智能体间的交互，降低通信开销，并理论验证了稳定性，仿真表明该方法能更紧密地控制编队构型。
This article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods.