no code implementations • 15 Apr 2024 • Isaac A. Spiegel, Nard Strijbosch, Robin de Rozario, Tom Oomen, Kira Barton
To demonstrate the primary contributions' validity and utility, this article also integrates PWA stable inversion with ILC in simulations based on a physical printhead positioning system.
no code implementations • 13 Apr 2024 • Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen
Block coordinate descent is an optimization technique that is used for estimating multi-input single-output (MISO) continuous-time models, as well as single-input single output (SISO) models in additive form.
no code implementations • 4 Mar 2024 • Max van Haren, Kentaro Tsurumoto, Masahiro Mae, Lennart Blanken, Wataru Ohnishi, Tom Oomen
Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations.
no code implementations • 22 Feb 2024 • Rogier Dinkla, Sebastiaan Mulders, Tom Oomen, Jan-Willem van Wingerden
Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC).
no code implementations • 2 Feb 2024 • Max van Meer, Gert Witvoet, Tom Oomen
Switched Reluctance Motors (SRMs) are cost-effective electric actuators that utilize magnetic reluctance to generate torque, with torque ripple arising from unaccounted manufacturing defects in the rotor tooth geometry.
no code implementations • 18 Jan 2024 • Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen
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 stability constraints can only be numerically verified, e. g., through solving Linear Matrix Inequalities.
no code implementations • 2 Jan 2024 • Rodrigo A. González, Koen Classens, Cristian R. Rojas, James S. Welsh, Tom Oomen
When identifying electrical, mechanical, or biological systems, parametric continuous-time identification methods can lead to interpretable and parsimonious models when the model structure aligns with the physical properties of the system.
no code implementations • 29 Sep 2023 • Max van Meer, Rodrigo A. González, Gert Witvoet, Tom Oomen
Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs.
no code implementations • 22 Sep 2023 • Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen
The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network.
no code implementations • 5 Jun 2023 • Max van Haren, Leonid Mirkin, Lennart Blanken, Tom Oomen
Fast-sampled models are essential for control design, e. g., to address intersample behavior.
no code implementations • 12 May 2023 • Koen Classens, W. P. M. H., Heemels, Tom Oomen
The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances.
no code implementations • 6 Apr 2023 • Rodrigo A. González, Koen Tiels, Tom Oomen
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control.
no code implementations • 6 Apr 2023 • Max van Meer, Emre Deniz, Gert Witvoet, Tom Oomen
Sensors in high-precision mechatronic systems require accurate calibration, which is achieved using test beds that, in turn, require even more accurate calibration.
no code implementations • 14 Mar 2023 • Johan Kon, Naomi de Vos, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
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 nonlinear friction.
no code implementations • 14 Mar 2023 • Max van Haren, Lennart Blanken, Tom Oomen
The aim of this paper is to develop an identification approach that directly identifies dynamically scheduled feedforward controllers for LPV motion systems from data.
no code implementations • 10 Mar 2023 • Rodrigo A. González, Koen Tiels, Tom Oomen
Sampling in control applications is increasingly done non-equidistantly in time.
no code implementations • 1 Nov 2022 • Max van Haren, Lennart Blanken, Tom Oomen
Feedforward for motion systems is getting increasingly more important to achieve performance requirements.
no code implementations • 26 Sep 2022 • Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
Unknown nonlinear dynamics often limit the tracking performance of feedforward control.
no code implementations • 23 Sep 2022 • Jilles van Hulst, Maurice Poot, Dragan Kostić, Kai Wa Yan, Jim Portegies, Tom Oomen
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput.
no code implementations • 14 Sep 2022 • Max van Meer, Gert Witvoet, Tom Oomen
Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance.
no code implementations • 12 Sep 2022 • Leontine Aarnoudse, Tom Oomen
To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments.
no code implementations • 12 Sep 2022 • Jan-Willem van Wingerden, Sebastiaan Mulders, Rogier Dinkla, Tom Oomen, Michel Verhaegen
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model.
no code implementations • 12 Sep 2022 • Leontine Aarnoudse, Johan Kon, Koen Classens, Max van Meer, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen
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.
no code implementations • 10 Feb 2022 • Johan Kon, Marcel Heertjes, Tom Oomen
An increasing trend in the use of neural networks in control systems is being observed.
no code implementations • 1 Feb 2022 • Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen
Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position.
no code implementations • 19 Jan 2022 • Max van Haren, Maurice Poot, Dragan Kostić, Robin van Es, Jim Portegies, Tom Oomen
Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control.
no code implementations • 10 Jan 2022 • Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics.
no code implementations • 7 Dec 2021 • Max van Meer, Maurice Poot, Jim Portegies, Tom Oomen
Feedforward control is essential to achieving good tracking performance in positioning systems.
no code implementations • 25 Nov 2021 • Johan Kon, Nard Strijbosch, Sjirk Koekebakker, Tom Oomen
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., in systems with package loss or when exploiting timestamped data from optical encoders.
no code implementations • 16 Nov 2021 • Leontine Aarnoudse, Tom Oomen
Data-driven iterative learning control can achieve high performance for systems performing repeating tasks without the need for modeling.
no code implementations • 16 Aug 2021 • Isaac A Spiegel, Nard Strijbosch, Tom Oomen, Kira Barton
Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models.
no code implementations • 20 Jul 2021 • Tom Bloemers, Roland Tóth, Tom Oomen
Synthesizing controllers directly from frequency-domain measurement data is a powerful tool in the linear time-invariant framework.