Paul van den Hof is Full Professor and Chair of the Control Systems (CS) Group at the Department of Electrical Engineering. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization. Van den Hof often collaborates closely with international academic and industrial partners. Van den Hof was General Chair of the 13th IFAC Symposium on System Identification in 2003. He has served as member of the IFAC Council (1999-2005, 2017-2020), Associate Editor and Editor of Automatica (1992-2005), and Vice-President of IFAC (2017-2020). He is a Fellow of IFAC and of IEEE, and Honorary Member of the Hungarian Academy of Sciences. He holds an ERC Advanced Research Grant on Dynamic Network Identification.
Paul van den Hof obtained an MSc from Eindhoven University of Technology (TU/e) in 1982 and a PhD from there in 1989. He started work at Delft University of Technology in 1986, where he was appointed as Full Professor in 1999. He was the founding co-director of the Delft Center for Systems and Control (DCSC), with appointments in the faculty of Mechanical, Maritime, and Materials Engineering, and the faculty of Applied Sciences. In 2011, he was appointed Full Professor at the Electrical Engineering Department of TU/e. From 2005-2015, he was Scientific Director of the National Research and Graduate School Dutch Institute of Systems and Control (DISC), and National Representative of the Dutch NMO in IFAC.
An instrumental variable method for closed-loop identification of coreless linear motors2018 Annual American Control Conference, (ACC2018) (2018)
Locating nonlinearity in mechanical systems: a dynamic network perspectiveNonlinear Dynamics, Volume 1 (2018)
On representations of linear dynamic networks18th IFAC Symposium on System Identification (SYSID 2018) (2018)
Parameter estimation of an electrochemistry-based Lithium-ion battery model using a two-step procedure and sensitivity analysisInternational Journal of Energy Research (2018)
Identifiability of linear dynamic networksAutomatica (2018)
- Selected topics in systems and control
- System identification
- Control systems
- Machine learning for Systems and Control
- Hoogleraar/wetenschappelijk directeur., TUD/Onderzoeksschool DISC