论文标题
具有仪器变量的基于数据的预测控制：与子空间预测控制的直接等效性
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control
论文作者
论文摘要
直接数据驱动的控制引起了很大的兴趣，因为它可以基于优化的控制而无需参数模型。本文提出了一种新的仪器变量〜（iv）方法，用于启用数据的预测控制（DEEPC），该方法可带来有利的降解效果，并证明了DEEPC和Subspace预测控制（SPC）之间的直接等效性。该方法依赖于沿着子空间识别算法的线条中特征方程的推导。提出了特定的IV选择，与未来噪声无关，但同时与数据矩阵高度相关。一项仿真研究表明，在存在过程和测量噪声的情况下，提出的算法的性能提高了。
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable~(IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.
