16 citations · 18 across the 5 of their papers we have counts for
5 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…
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…
Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control
Johan Kon, Naomi de Vos, Dennis Bruijnen +3
Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and…
Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics
Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven +2
Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate thes…
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach
Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven +2
Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknow…