From the 1 of 8 linked papers with an AI index.
8 papers
A subspace approach to data-driven predictive control for linear parameter-varying systems
Federico Porcari, Chris Verhoek, Valentina Breschi +2
The paper proposes a subspace-based data‑driven predictive control scheme for linear parameter‑varying (LPV) systems that avoids explicit model identification and offers reduced co…
A Quantitative Framework for Navigating Controller Design Tradeoffs under Computational Constraints
Chris Verhoek, Nikolai Matni
Computational constraints permeate the controller design process, and yet are rarely treated as explicit design constraints. Towards addressing this gap, we propose a quantitative…
Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Bendegúz Györök, Roel Drenth, Chris Verhoek +3
The integration of first-principles models with learning-based components, i.e., model augmentation, has gained increasing attention, as it offers higher model accuracy and faster…
A Linear Parameter-Varying Approach to Data Predictive Control
Chris Verhoek, Julian Berberich, Sofie Haesaert +2
By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-fee…
Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
Chris Verhoek, Ivan Markovsky, Roland Tóth
We consider the problem of estimating missing values in trajectories of linear parameter-varying (LPV) systems. We solve this interpolation problem for the class of shifted-affine…
A behavioral approach for LPV data-driven representations
Chris Verhoek, Ivan Markovsky, Sofie Haesaert +1
In this paper, we present a data-driven representation for linear parameter-varying (LPV) systems, which can be used for direct data-driven analysis and control of such systems. Sp…