3 papers
eess.SY2026
A subspace approach to data-driven predictive control for linear parameter-varying systems
Federico Porcari, Chris Verhoek, Valentina Breschi +2
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, w…
eess.SY2026
Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
Roel Drenth, Jan H. Hoekstra, Maarten Schoukens +1
Identifying control-friendly models of nonlinear systems remains one of the major challenges at the intersection of system identification and control. The Linear Parameter-Varying…
math.OC2026
Efficient sparse GP-MPC with accurate mean and variance propagation applied for quadcopter flight control
Giannis Badakis, Mircea Lazar, Roland Toth
This paper presents a computationally efficient approach for Gaussian process model predictive control (GP-MPC), where Gaussian process (GP) regression is used to complement a base…