发表机构
Eindhoven University of Technology; TU Dortmund University（埃因霍温理工大学; 多特蒙德工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究数据驱动预测控制中数据收集问题，指出仅满足秩条件不足以实现满意闭环性能，聚焦特定场景，通过频域分析为控制导向实验设计奠定基础，数值结果显示忽视控制目标会使闭环系统无法跟踪参考。
AI 中文摘要
在数据驱动控制中，理解如何收集对控制有意义的数据至关重要。现有方法主要依靠秩条件的满足来评估实验质量，但我们表明满足它并不总是足以实现令人满意的闭环性能。聚焦于不能使用类似白噪声激励进行数据收集的场景，我们研究了线性行为表示在频域的影响。该分析表明数据必须既满足秩条件又激发控制目标的感兴趣频率，从而为面向直接数据驱动方法的控制导向实验设计奠定基础。这些发现反映在我们的数值结果中。依赖满足秩条件但忽略跟踪控制目标的数据的预测控制器会导致闭环系统无法跟踪所选参考。
英文摘要
Understanding how to collect data that is "meaningful" for control purposes is of paramount importance in data-driven control. While existing approaches have primarily relied on the satisfaction of a rank condition to assess the quality of an experiment, we show that satisfying it is not always sufficient to achieve satisfactory closed-loop performance. Focusing on scenarios where white-noise-like excitation cannot be used for data collection, we examine the frequency-domain implications of linear behavioral representation. This analysis demonstrates that leakage effects are the main driver for data to represent the dynamics of the system. These findings are reflected in our numerical results. Data-enabled predictive controllers built on datasets with insufficient bandwidth, despite fulfilling standard rank conditions, suffer from severe ill-conditioning and fail to achieve reference tracking.
CommentsAccepted at the 65th IEEE Conference on Decision and Control (invited session)
