论文标题
数据驱动的Koopman控制器综合基于扩展的$ \ Mathcal {h} _2 $ norm demarization
Data-Driven Koopman Controller Synthesis Based on the Extended $\mathcal{H}_2$ Norm Characterization
论文作者
论文摘要
本文基于Koopman操作员和扩展的$ \ Mathcal {H} _2 $ narm narm表征离散时间线性系统的规范表征。我们将动态系统建模为多层集合，这些集合是从多个通过Koopman操作员的有限近似获得的多个数据驱动的线性模型得出的，然后用于设计强大的反馈控制器与$ \ Mathcal {H} _2 _2 $ norm norm norm norm deminization。 $ \ MATHCAL {H} _2 $规范表征的使用旨在处理由于问题的数据驱动设置的性质而引起的模型不确定性。通过数值模拟研究了提出的控制器合成的有效性。
This paper presents a new data-driven controller synthesis based on the Koopman operator and the extended $\mathcal{H}_2$ norm characterization of discrete-time linear systems. We model dynamical systems as polytope sets which are derived from multiple data-driven linear models obtained by the finite approximation of the Koopman operator and then used to design robust feedback controllers combined with the $\mathcal{H}_2$ norm characterization. The use of the $\mathcal{H}_2$ norm characterization is aimed to deal with the model uncertainty that arises due to the nature of the data-driven setting of the problem. The effectiveness of the proposed controller synthesis is investigated through numerical simulations.
