标题： 向联邦DeePC迈进：从相似系统中借用数据 显示英文标题

标题： Toward Federated DeePC: borrowing data from similar systems

摘要： 数据驱动的预测控制方法，一般而言以及数据使能的预测控制（DeePC），特别地，利用原始输入/输出轨迹矩阵来进行控制设计。这些数据通常仅从被控系统中收集。然而，随着连接性的增加和（批量生产）系统的固有相似性，可能会产生大量可以用于执行控制任务的信息。鉴于此，我们提出了一种DeePC的初步联邦扩展，该方法利用多个相似系统的输入/输出轨迹组合来进行预测控制。通过一系列数值例子的支持，我们的分析揭示了利用相似系统信息的潜在好处及其可能的缺点。 显示英文摘要

摘要： Data-driven predictive control approaches, in general, and Data-enabled Predictive Control (DeePC), in particular, exploit matrices of raw input/output trajectories for control design. These data are typically gathered only from the system to be controlled. Nonetheless, the increasing connectivity and inherent similarity of (mass-produced) systems have the potential to generate a considerable amount of information that can be exploited to undertake a control task. In light of this, we propose a preliminary federated extension of DeePC that leverages a combination of input/output trajectories from multiple similar systems for predictive control. Supported by a suite of numerical examples, our analysis unveils the potential benefits of exploiting information from similar systems and its possible downsides.