6 citations · 9 across the 10 of their papers we have counts for
10 papers
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification
Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen +3
Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stabi…
State Derivative Normalization for Continuous-Time Deep Neural Networks
Jonas Weigand, Gerben I. Beintema, Jonas Ulmen +4
The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper nor…
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
Patrick J. W. Koelewijn, Rajiv Sing, Peter Seiler +1
In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step pro…
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…
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
Yuhan Liu, Pengyu Wang, Chang-Hun Lee +1
One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlin…
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen +3
The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an…
Equilibrium-Independent Control of Continuous-Time Nonlinear Systems via the LPV Framework -- Extended Version
Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.…
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the…
Minimal realizations of input-output behaviors by LPV state-space representations with affine dependency
Mihály Petreczky, Roland Tóth, Guillaume Mercère
The paper makes the first steps towards a behavioral theory of LPV state-space representations with an affine dependency on scheduling, by characterizing minimality of such state-s…
Affine LPV systems: realization theory, input-output equations and relationship with linear switched systems
Mihaly Petreczky, Guillaume Mercère, Roland Tóth
We formulate a Kalman-style realization theory for discrete-time affine LPV systems. By an affine LPV system we mean an LPV system whose matrices are affine functions of the schedu…