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Sampling Strategies for Data-Driven Inference of Input-Output System Properties
IEEE Transactions on Automatic Control ( IF 6.8 ) Pub Date : 2020-01-01 , DOI: 10.1109/tac.2020.2994894
Anne Koch , Jan Maximilian Montenbruck , Frank Allgower

Due to their relevance in controller design, we consider the problem of determining the $\mathcal{L}^2$-gain, passivity properties and conic relations of an input-output system. While, in practice, the input-output relation is often undisclosed, input-output data tuples can be sampled by performing (numerical) experiments. Hence, we present sampling strategies for discrete time and continuous time linear time-invariant systems to iteratively determine the $\mathcal{L}^2$-gain, the shortage of passivity and the cone with minimal radius that the input-output relation is confined to. These sampling strategies are based on gradient dynamical systems and saddle point flows to solve the reformulated optimization problems, where the gradients can be evaluated from only input-output data samples. This leads us to evolution equations, whose convergence properties are then discussed in continuous time and discrete time.

中文翻译:

输入输出系统属性数据驱动推理的抽样策略

由于它们在控制器设计中的相关性,我们考虑确定输入-输出系统的 $\mathcal{L}^2$-增益、无源特性和圆锥关系的问题。虽然在实践中,输入-输出关系通常是未公开的,但可以通过执行(数值)实验对输入-输出数据元组进行采样。因此,我们提出了离散时间和连续时间线性时不变系统的采样策略,以迭代地确定 $\mathcal{L}^2$-增益、无源性的不足以及输入输出关系为最小半径的锥体限制于。这些采样策略基于梯度动力系统和鞍点流来解决重新制定的优化问题,其中梯度可以仅从输入-输出数据样本进行评估。这将我们引向进化方程,
更新日期:2020-01-01
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