Implicit predictors in regularized data-driven predictive control

We introduce the notion of implicit predictors, which characterize input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly enforced as an equality constraint (as in traditional model or subspace control). To demonstrate this concept, we derive and analyze predictors for some basic data-driven (DPC) schemes, offers new perspective on popular approach that may form basis modified DPC schemes further theoretical insights.