16 citations · 20 across the 5 of their papers we have counts for
5 papers
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…
Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution
Johan Kon, Marcel Heertjes, Tom Oomen
An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural n…
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…