AI 中文总结
研究传感器融合系统中事件触发参数估计，引入回归器驱动局部触发规则，在持续激励条件下推导出保证全局指数收敛的设计不等式，基于时变李雅普诺夫函数分析，仿真显示节省通信并保持收敛性。
AI 中文摘要
本文研究传感器融合系统中的事件触发参数估计，传感器将测量值传输到基于梯度的估计器。我们引入了一种回归器驱动的局部触发规则，该规则无需当前参数估计的知识，仅依赖于回归器信号。在聚合回归器的持续激励条件下，我们推导出估计器增益和事件阈值的显式设计不等式，保证全局指数收敛。分析基于时变李雅普诺夫函数。我们还提供了回归器动态的充分条件，以强制事件间时间的均匀下限，排除芝诺行为。仿真显示在保持指数收敛的同时节省了大量通信。
英文摘要
This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.
CommentsThis work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. Accepted for presentation at the 23rd IFAC World Congress 2026
