Bounding Uncertainty in State Estimation Under Dynamic Event-Triggered Communication
针对动态事件触发通信导致状态估计不确定性发散的问题，提出一种结合离散测量与事件间隐含信息的新型估计器，保证均方误差一致有界，并自适应减少通信次数。
In the stochastic estimation context, the absence of measurement information at the state estimator during large intervals can cause a divergence in the uncertainty of the estimates. This issue is aggravated when strategies to reduce communication, such as event-triggering mechanisms (ETMs), are used if an appropriate design is not made. Particularly, dynamic ETMs (DETMs) may exhibit this problem, since they are designed to further reduce the number of communication instants. Motivated by this problem, we propose a novel state estimator that integrates discrete transmitted measurements and implicit information between events provided by the proposed DETM. Our proposal guarantees a uniformly bounded mean-squared error in the stochastic context, regardless of transmission instants. Moreover, compared to static ETMs, our proposal adaptively reduces the number of transmissions according to the behavior of the measured signal. Our proposal’s advantages are verified formally and through several numerical experiments.