Implicit predictors in regularized data-driven predictive control
Systems and Control
2025-05-27 v2 Systems and Control
Optimization and Control
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.
Cite
@article{arxiv.2307.10750,
title = {Implicit predictors in regularized data-driven predictive control},
author = {Manuel Klädtke and Moritz Schulze Darup},
journal= {arXiv preprint arXiv:2307.10750},
year = {2025}
}
Comments
This paper is a preprint of a contribution to the IEEE Control Systems Letters. 6 pages, 2 figures