Implicit predictors in regularized data-driven predictive control

TL;DR

This paper introduces the concept of implicit predictors in data-driven predictive control, providing a new perspective on how input-output behavior can be characterized without explicit constraints, and analyzing their implications.

Contribution

The paper defines and analyzes implicit predictors in DPC, offering a novel theoretical framework that could lead to improved control schemes and deeper understanding.

Findings

Implicit predictors characterize input-output behavior without explicit constraints

Analysis of basic DPC schemes using implicit predictors

Potential for developing modified DPC schemes based on this concept

Abstract

We introduce the notion of implicit predictors, which characterize the 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 predictive control). To demonstrate this concept, we derive and analyze implicit predictors for some basic data-driven predictive control (DPC) schemes, which offers a new perspective on this popular approach that may form the basis for modified DPC schemes and further theoretical insights.