In this paper, network of agents with identical dynamics is considered. The agents are assumed to be fed by self and neighboring output measurements, while the states are not available for measuring. Viewing distributed estimation as dual to the distributed LQR problem, a distributed observer is proposed by exploiting two complementary distributed LQR methods. The first consists of a bottom-up approach in which optimal interactions between self-stabilizing agents are defined so as to minimize an upper bound of the global LQR criterion. In the second (top-down) approach, the centralized optimal LQR controller is approximated by a distributed control scheme whose stability is guaranteed by the stability margins of LQR control. In this paper, distributed observer which minimizes an upper bound of a deterministic performance criterion, is proposed by solving a dual LQR problem using bottom-up approach. The cost function is defined by considering minimum-energy estimation theory where the weighting matrices have deterministic interpretation. The presented results are useful for designing optimal or near-optimal distributed control/estimation schemes.

中文翻译：

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基于分布式 LQR 的大规模多智能体网络观察器设计

在本文中，考虑了具有相同动态的代理网络。假设代理由自身和相邻输出测量提供，而状态不可用于测量。将分布式估计视为分布式 LQR 问题的对偶，通过利用两种互补的分布式 LQR 方法提出了一种分布式观察器。第一个由自下而上的方法组成，其中定义了自稳定剂之间的最佳相互作用，以最小化全局 LQR 标准的上限。在第二种（自顶向下）方法中，集中式最优 LQR 控制器由分布式控制方案近似，其稳定性由 LQR 控制的稳定性裕度保证。在本文中，分布式观察器最小化确定性性能标准的上限，是通过使用自下而上的方法解决双 LQR 问题提出的。成本函数是通过考虑最小能量估计理论来定义的，其中加权矩阵具有确定性解释。所呈现的结果对于设计最优或接近最优的分布式控制/估计方案是有用的。