10 citations · 15 across the 7 of their papers we have counts for
9 papers
Robust Data-Driven Controller Synthesis from Sampled and Uncertain Frequency Response Estimates using Integral Quadratic Constraints
Luuk Marceau Spin, Maurice W. P. M. H. Heemels, Nathan van de Wouw +1
In this paper, we develop a novel MIMO controller synthesis method that relies solely on frequency-domain data of discrete-time systems. The approach is based on an iterative desig…
Iteratively Convex Frequency Data-Driven Synthesis of Structured MIMO Controllers
Luuk Marceau Spin, Thom Samuels, Dragan Kostic +3
Frequency-response-function-based controller design enables synthesis directly from measured data, avoiding the need for accurate parametric plant models. This paper presents a gen…
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…
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…
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…
Learning-based model augmentation with LFRs
Jan H. Hoekstra, Chris Verhoek, Roland Tóth +1
Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. In particular, recent encoder-based methods for artifici…
Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control
Chris Verhoek, Jaap Eising, Florian Dörfler +1
A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose param…
Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control
Yorick Broens, Hans Butler, Roland Tóth
Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput,…
Unified Behavioral Data-Driven Performance Analysis: A Generalized Plant Approach
L. M. Spin, C. Verhoek, W. P. M. H. Heemels +2
In this paper, we present a novel approach to combine data-driven non-parametric representations with model-based representations of dynamical systems. Based on a data-driven form…