16 citations · 19 across the 8 of their papers we have counts for
8 papers
Closed-loop Optimal Fault Detection for Uncertain Systems
Koen Classens, Tjeerd Ickenroth, Jeroen van de Wijdeven +1
Faults compromise the reliability and safety of complex engineering systems. The aim of this article is to address the problem of robust fault detection filter design for continuou…
Nullspace-based Fault Diagnosis for Closed-Loop Mechatronic Systems with Application to Semiconductor Equipment
Koen Classens, Jeroen van de Wijdeven, Maurice Heemels +1
Fault detection and isolation (FDI) systems are critical for modern mechatronic production equipment, as their continuous operation is heavily dependent on the ability to detect an…
Identification of additive multivariable continuous-time systems
Maarten van der Hulst, Rodrigo González, Koen Classens +3
Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this paper is to develop a parametric…
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…