5 citations · 5 across the 4 of their papers we have counts for
7 papers
On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control
Maurice Poot, Jim Portegies, Tom Oomen
Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this…
Gaussian Process Repetitive Control for Suppressing Spatial Disturbances
Noud Mooren, Gert Witvoet, Tom Oomen
Motion systems are often subject to disturbances such as cogging, commutation errors, and imbalances, that vary with velocity and appear periodic in time for constant operating vel…
Commutation-Angle Iterative Learning Control for Intermittent Data: Enhancing Piezo-Stepper Actuator Waveforms
Leontine Aarnoudse, Nard Strijbosch, Edwin Verschueren +1
Piezo-stepper actuators are used in many nanopositioning systems due to their high resolution, high stiffness, fast response, and the ability to position a mover over an infinite s…
Temperature-Dependent Modeling of Thermoelectric Elements
Enzo Evers, Rens Slenders, Rob van Gils +1
Active thermal control is crucial in achieving the required accuracy and throughput in many industrial applications, e.g., in the medical industry, high-power lighting industry, an…
On Frequency Response Function Identification for Advanced Motion Control
Enzo Evers, Robbert Voorhoeve, Tom Oomen
A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutio…
Learning for Advanced Motion Control
Tom Oomen
Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatr…