Paul Van den Hof
Contactp.m.j.vandenhof@ tue.nl +31 40 247 3839 Flux 5.134
Paul van den Hof is Full Professor 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.
In engineering and many other domains of science, dynamic models are essential for model-based simulation, monitoring, control and optimization.”
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.
On the informativity of direct identification experiments in dynamical networksAutomatica (2023)
Local identification in diffusively coupled linear networks61st IEEE Conference on Decision and Control, CDC 2022 (2023)
Single module identifiability in linear dynamic networks with partial excitation and measurementIEEE Transactions on Automatic Control (2023)
A frequency domain approach for local module identification in dynamic networksAutomatica (2022)
A scalable multi-step least squares method for network identification with unknown disturbance topologyAutomatica (2022)
- System identification
- Selected topics in systems and control
- Control systems
No ancillary activities