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
Application domains range from multi-physics high-tech/mechatronic systems, automotive systems, energy and power converter systems to industrial process control systems. In each of these domains CS collaborates with leading companies contributing our fundamental knowledge to their innovation roadmaps.
CS actively participates in the research program of the national research school DISC (Dutch Institute of Systems and Control) and significantly contributes to the “Smart and Sustainable Systems” program of the EE department. Cooperative links exist with the EE groups EPE and EES and the ME groups CST and DC.
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 (C3S)
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.
Work with us!
CURRENT OPEN POSITION
Within the project SYSDYNET the following open positions are available:
Software developer (Researcher / research engineer)
We are looking for research fellows with a programming-oriented mindset to
contribute to the development of a Matlab toolbox for dynamic network identification.
Post-doc 'Machine learning in dynamic network modeling'
For more information on these positions, contact Prof. Paul Van den Hof.
Meet some of our Researchers
Henk jan Bergveld
Paul Van den Hof
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
Battery State-of-Charge and temperature estimation.
Ageing aware fast charging of batteries.
Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management.
Implementation of powertrain control for economic, low real driving emissions and fuel consumption.
Data-Driven Modeling in Dynamic Networks.
Our most recent peer reviewed publications
Ultra-stable sodium metal-iodine batteries enabled by an in-situ solid electrolyte interphaseNano Energy (2019)
Modeling of reactive batch distillation columns for controlComputers and Chemical Engineering (2019)
Constrained order observer design for disturbance decoupled output estimationIEEE Control Systems Letters (2019)
Disturbance feedforward control for active vibration isolation systems with internal isolator dynamicsJournal of Sound and Vibration (2018)
A novel Krylov method for model order reduction of quadratic bilinear systems57th IEEE Conference on Decision and Control (CDC 2018) (2018)