论文标题
关于支持数据的预测控制与子空间预测控制之间的关系
On the relationship between data-enabled predictive control and subspace predictive control
论文作者
论文摘要
支持数据的预测控制（DEEPC）是一种最近提出的方法，它结合了单个优化问题中的系统识别，估计和控制，仅需要记录的已检查系统的输入/输出数据。子空间预测控制（SPC）方法相同的前提，其中从与DEEPC所需的相同数据中识别多步预测模型。然后，该模型用于制定类似的最佳控制问题。在这项工作中，我们研究了DIEDC与SPC之间的关系。我们的主要贡献是表明，在确定性情况下，SPC等同于DEEPC。我们还在特殊情况下显示了两种方法的等效性，用于非确定性公式。我们研究了DIEDC的优势和缺点，而不是在没有测量噪声的情况下进行SPC，并以模拟示例进行了说明。
Data-enabled predictive control (DeePC) is a recently proposed approach that combines system identification, estimation and control in a single optimization problem, for which only recorded input/output data of the examined system is required. The same premise holds for the subspace predictive control (SPC) method in which a multi-step prediction model is identified from the same data as required for DeePC. This model is then used to formulate a similar optimal control problem. In this work we investigate the relationship between DeePC and SPC. Our primary contribution is to show that SPC is equivalent to DeePC in the deterministic case. We also show the equivalence of both methods in a special case for the non-deterministic formulation. We investigate the advantages and shortcomings of DeePC as opposed to SPC with and without measurement noise and illustrate them with a simulation example.
