Enabling smart, high-performance operation of technical systems across technology domains
The mission of the group is to be an internationally recognized centre of research in systems and control, where contributions to fundamental theory are combined with advancing innovative technological applications in a selected number of domains, in cooperation with relevant industrial partners. The fundamental research of CS is directed towards the following research lines:
• Data-driven modelling in dynamic networks
• Modelling and control of linear parameter varying systems
• Spatial-temporal multi-physics systems and model reduction
• Data analytics and machine learning
• Constrained and interconnected systems
• Model-based control and optimization
• Control of cyber-physical systems
Autonomous Motion Control Lab
The Autonomous Motion Control (AMC) Lab is a testbed for new solutions of autonomous motion problems.
Constrained Control of Complex Systems
The C3S Lab focuses on stability and control of complex dynamical systems subject to constraints. Complex dynamical systems typically...
Control of high-precision mechatronic systems
This Lab focusses on control techniques in lithographic scanners, in particular related to high-accuracy positioning systems.
Dynamic Networks: Data-Driven Modeling and Control
We develop methods and tools for the modeling of dynamic networks on the basis of operational data, to be used as a basis for model-based...
Dynamics and Control for Electrified Automotive Systems
The research covers several aspects of optimization and control for automotive systems.
Formal methods for control of cyber-physical systems
In our lab, we develop theory and engineering methods for the design and formal verification of control in cyber-physical systems (CPS).
Machine Learning for Modelling and Control
Focus on data-driven modelling (identification) and control of complex physical/chemical systems, in particular in the high-tech and process...
Smart Process Operations and Control Lab
SPROC Lab focuses on challenges critical for the dynamic and flexible operation of chemical processes.
Spatial-Temporal Systems for Control
This research focuses on the modeling of multi-physics dynamic systems.
Meet some of our Researchers
Victor Reyes Dreke
Birgit van Huijgevoort
Clarisse Bosman Barros
Paul Van den Hof
Carlos Gonzalez Rojas
Francis Le Roux
Ilja van Oort
Henk jan Bergveld
Alejandro Marquez Ruiz
Control Systems has four main application domains: High Tech Systems, Automotive, Process industry and Energy. In each of these domains CS collaborates with leading companies contributing our fundamental knowledge to their innovation roadmaps. Often these companies are dominant market players, sometimes world leaders. With most of them CS has already built up a relationship over many years.
Below you can find the listing of most of our industry partners, a description of the innovation topics and a link to project details.
Some of our projects
Development of efficient and environmental friendlyLONG distance powertrain for heavy dUty trucks aNd coaches
New Energy Outlooks for the Netherlands
Physics-guided neural controllers for compensating parasitic forces in high-precision mechatronics
REACT-EU Battery Competence Center
Our most recent peer reviewed publications
On the informativity of direct identification experiments in dynamical networksAutomatica (2023)
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems61st IEEE Conference on Decision and Control, CDC 2022 (2023)
NARX Identification using Derivative-Based Regularized Neural Networks61st IEEE Conference on Decision and Control, CDC 2022 (2023)
Local identification in diffusively coupled linear networks61st IEEE Conference on Decision and Control, CDC 2022 (2023)
Message passing-based system identification for NARMAX models61st IEEE Conference on Decision and Control, CDC 2022 (2023)