16 citations · 22 across the 11 of their papers we have counts for
11 papers
Automatic Basis Function Selection in Iterative Learning Control: A Sparsity-Promoting Approach Applied to an Industrial Printer
Tjeerd Ickenroth, Max van Haren, Johan Kon +3
Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past i…
A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
Johan Kon, Roland Tóth, Jeroen van de Wijdeven +2
A minimal state-space (SS) realization of an identified linear parameter-varying (LPV) input-output (IO) model usually introduces dynamic and nonlinear dependency of the state-spac…
Orthogonal projection-based regularization for efficient model augmentation
Bendegúz M. Györök, Jan H. Hoekstra, Johan Kon +3
Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical…
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…
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Leontine Aarnoudse, Johan Kon, Koen Classens +5
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. Th…
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…
Intermittent Sampling in Repetitive Control: Exploiting Time-Varying Measurements
Johan Kon, Nard Strijbosch, Sjirk Koekebakker +1
The performance increase up to the sensor resolution in repetitive control (RC) invalidates the standard assumption in RC that data is available at equidistant time instances, e.g.…