论文标题
数据驱动的分析和控制在高度抽样下的连续时间系统
Data-driven analysis and control of continuous-time systems under aperiodic sampling
论文作者
论文摘要
我们通过仅使用嘈杂的数据，但没有模型知识来研究状态反馈下未知的连续时间系统的稳定性分析和控制器设计。我们首先得出了与测量数据和假定的噪声结合的所有线性时间不变的连续时间系统的新型数据依赖性参数化。基于此参数化，并通过将鲁棒控制理论的工具和采样数据控制的时间延迟方法组合在一起，我们在给定的状态反馈增益下，在最大采样间隔（MSI）上计算最大采样间隔（MSI）的下限，除此之外，我们设计了可能显示出可能大的MSI的控制器。我们的方法可确保与测量数据一致的所有系统的稳定性。作为技术贡献，所提出的方法将现有的方法嵌入了对DATA控制的现有方法中的一般稳健控制框架，该框架可以直接扩展到基于模型的鲁棒控制器设计，以在一般不确定性描述下用于不确定的时间延迟系统。
We investigate stability analysis and controller design of unknown continuous-time systems under state-feedback with aperiodic sampling, using only noisy data but no model knowledge. We first derive a novel data-dependent parametrization of all linear time-invariant continuous-time systems which are consistent with the measured data and the assumed noise bound. Based on this parametrization and by combining tools from robust control theory and the time-delay approach to sampled-data control, we compute lower bounds on the maximum sampling interval (MSI) for closed-loop stability under a given state-feedback gain, and beyond that, we design controllers which exhibit a possibly large MSI. Our methods guarantee the stability properties robustly for all systems consistent with the measured data. As a technical contribution, the proposed approach embeds existing methods for sampled-data control into a general robust control framework, which can be directly extended to model-based robust controller design for uncertain time-delay systems under general uncertainty descriptions.
