Event-Triggered Distributed Moving Horizon State Estimation of Linear Systems
针对由多个子系统组成的线性系统，提出了一种事件触发的分布式状态估计机制，包含两种能处理扰动和噪声约束的滚动时域估计算法，通过事件调度器减少处理器使用和通信频率，并证明估计误差收敛有界。
In this article, an event-triggered distributed state estimation mechanism is proposed for general linear systems that comprise several subsystems. Two distributed moving horizon estimation (MHE) algorithms that can handle constraints on disturbances and noise are proposed. An event scheduler is exploited to govern the evaluation of the estimators and networked information exchange between the plant and the estimators, such that good estimates can be provided while both the usage of processors and networked communication frequency can be reduced. The estimation error provided by the event-triggered estimation mechanism is proven to be convergent and bounded. A numerical example and a chemical process example are used to verify the effectiveness and applicability of the proposed method.