用于线性系统的即插即用分布式状态估计
Plug-and-play distributed state estimation for linear systems
AI总结：
本文提出了一种用于大规模线性系统的即插即用分布式状态估计方法，通过局部状态估计器重构子系统状态，并保证误差受限且无扰动时收敛到零。
摘要：
本文提出了一种用于大规模线性系统的状态估计器，该系统由受有界扰动影响的状态耦合子系统交互描述。我们为每个子系统配备局部状态估计器（LSE），仅利用父子系统部分信息进行状态重构。此外，我们提供了保证估计误差被限制在预设多面体集内并在无扰动时收敛到零的条件。值得注意的是，LSE的设计被转化为一个优化问题，仅需相应子系统及其父系统的数据。这使得能够以即插即用（PnP）方式合成LSE，即当子系统被添加时，整个估计器的更新仅需设计该子系统及其父系统的LSE。理论结果通过机械系统的数值实验得到验证。
英文摘要：
This paper proposes a state estimator for large-scale linear systems described by the interaction of state-coupled subsystems affected by bounded disturbances. We equip each subsystem with a Local State Estimator (LSE) for the reconstruction of the subsystem states using pieces of information from parent subsystems only. Moreover we provide conditions guaranteeing that the estimation errors are confined into prescribed polyhedral sets and converge to zero in absence of disturbances. Quite remarkably, the design of an LSE is recast into an optimization problem that requires data from the corresponding subsystem and its parents only. This allows one to synthesize LSEs in a Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of the whole estimator requires at most the design of an LSE for the subsystem and its parents. Theoretical results are backed up by numerical experiments on a mechanical system.
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