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20232026
most citedIdentification of additive multivariable continuous-time systems

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

eess.SY2026

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…

eess.SY2026

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…

eess.SP20251 cited

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…

eess.SY2024

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…

eess.SY2023

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…

eess.SY2023

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…