标题： 混合数据驱动的预测控制：将模型知识融入DeePC 显示英文标题

标题： Hybrid Data-enabled Predictive Control: Incorporating model knowledge into the DeePC

摘要： 预测控制可以是基于数据的（例如，数据使能的预测控制，或DeePC）或者是基于模型的（模型预测控制）。在本文中，我们旨在弥合两者之间的差距，通过研究仅部分模型可用的情况，即把模型知识融入DeePC。在某些情况下，这相对于纯粹的数据方法具有优势，例如在噪声和计算成本方面，以及应用于某些线性时变和非线性系统。我们提出了一种利用部分模型知识的方法，我们称之为混合数据使能预测控制（HDeePC），并在无噪声、LTI的情况下证明了可行集等价性和等效闭环行为。最后，两个例子展示了HDeePC的潜力。 显示英文摘要

摘要： Predictive control can either be data-based (e.g. data-enabled predictive control, or DeePC) or model-based (model predictive control). In this paper we aim to bridge the gap between the two by investigating the case where only a partial model is available, i.e. incorporating model knowledge into DeePC. This has potential advantages over a purely data-based approach in terms of noise and computational expense in some cases, as well as applications to certain linear time-varying and nonlinear systems. We formulate an approach to take advantage of partial model knowledge which we call hybrid data-enabled predictive control (HDeePC) and prove feasible set equivalence and equivalent closed-loop behavior in the noiseless, LTI case. Finally, two examples illustrate the potential of HDeePC.