An event-triggered robust state estimation problem for linear time-varying systems subject to Gaussian noises and non-stochastic unknown exogenous inputs is investigated in this work. To design the estimator, the state estimation problem is formulated as an optimization problem with a risk-sensitive cost function. This problem is solved by constructing a reference probability measure, under which the cost function has a simpler form and an information state can be developed. The obtained robust state estimator is shown to have a recursive form parameterized by a Riccati-type time-varying matrix equation. The effectiveness of the proposed event-based robust state estimator is illustrated with numerical examples.

在这项工作中，研究了高斯噪声和非随机未知外源输入的线性时变系统的事件触发鲁棒状态估计问题。为了设计估计器，将状态估计问题表述为具有风险敏感成本函数的优化问题。通过构建参考概率度量可以解决此问题，在该度量下，成本函数具有更简单的形式并且可以开发信息状态。所获得的鲁棒状态估计量显示为具有由Riccati型时变矩阵方程参数化的递归形式。数值实例说明了所提出的基于事件的鲁棒状态估计器的有效性。