论文标题
数据驱动的输入重建和实验验证
Data-driven input reconstruction and experimental validation
论文作者
论文摘要
本文讨论了基于Willems的基本引理的数据驱动输入重建问题，其中未知输入估计器（UIE）直接由历史I/O数据构建。仅给定输出测量值，输入是由UIE估算的，UIE估算了该输入在不知道初始条件的情况下渐近地收敛到真实输入。开环和闭环UIE都是基于Lyapunov条件和Luenberger-Observer-type反馈的开发，研究了其收敛性能。进行了一项实验研究，证明了闭环UIE通过测量的二氧化碳水平估算EPFL校园建筑物的占用的功效。
This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data. Given only output measurements, the inputs are estimated by the UIE, which is shown to asymptotically converge to the true input without knowing the initial conditions. Both open-loop and closed-loop UIEs are developed based on Lyapunov conditions and the Luenberger-observer-type feedback, whose convergence properties are studied. An experimental study is presented demonstrating the efficacy of the closed-loop UIE for estimating the occupancy of a building on the EPFL campus via measured carbon dioxide levels.
