论文标题
稀疏感应和最佳精度：鲁棒$ \ Mathcal {h} _ {\ infty} $具有模型不确定性的最佳观察者设计
Sparse Sensing and Optimal Precision: Robust $\mathcal{H}_{\infty}$ Optimal Observer Design with Model Uncertainty
论文作者
论文摘要
我们提出了一个框架，该框架结合了估计问题的三个方面，即在模型不确定性的存在下，稀疏的传感器配置，最佳精度和鲁棒性。该问题是在$ \ Mathcal {H} _ {\ infty} $最佳观察者设计框架中提出的。我们考虑系统中两种类型的不确定性，即结构化仿射和非结构化的不确定性。目的是设计一个具有给定$ \ Mathcal {h} _ {\ infty} $性能索引的观察者，传感器数量最少和最小的精度值，同时保证所有可允许的不确定性的性能。该问题被视为凸优化问题，但要受线性矩阵不等式的影响。数值模拟证明了这项工作中介绍的理论结果的应用。
We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model uncertainty. The problem is formulated in the $\mathcal{H}_{\infty}$ optimal observer design framework. We consider two types of uncertainties in the system, i.e. structured affine and unstructured uncertainties. The objective is to design an observer with a given $\mathcal{H}_{\infty}$ performance index with minimal number of sensors and minimal precision values, while guaranteeing the performance for all admissible uncertainties. The problem is posed as a convex optimization problem subject to linear matrix inequalities. Numerical simulations demonstrate the application of the theoretical results presented in this work.
