Implicit predictors in regularized data-driven predictive 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.

This paper is a preprint of a contribution to the IEEE Control Systems Letters. 6 pages, 2 figures

References in corpus (2)

• Data-Driven Model Predictive Control with Stability and Robustness Guarantees
• A trajectory-based framework for data-driven system analysis and control

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• On the equivalence of direct and indirect data-driven predictive control approaches
• Extending direct data-driven predictive control towards systems with finite control sets
• On data usage and predictive behavior of data-driven predictive control with 1-norm regularization