Henk van Waarde
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Affiliation |
I am an Assistant Professor at the University of Groningen, where I work in the Systems, Control and Optimization group.
I am also a member of the Jan C. Willems Center for Systems and Control and the Centre for Data Science and Systems Complexity.
For a short bio, see this page. Also feel free to browse through my publications, presentations and updates linked below.
News
May 2026: I co-lectured a course within the program of the EECI International Graduate School on Control at TU Hamburg!
April 2026: Co-lectured a graduate course at Southeast University Nanjing, and attended the Tokyo Workshop on Data-Driven Control!
July 2025: Check out my new book on data-driven control, co-authored with Kanat Camlibel and Harry Trentelman!
June 2025: I gave a lecture at the DISC summer school on experiment design for data-driven modeling and control.
April 2025: Co-lectured a 5-day course at IIT Mandi, India. All handouts are online!
Research
My research interests lie in the field of systems and control. My aim is to develop a systems and control theory that is grounded on measured data. I work towards mapping raw data into models and control policies with rigorous guarantees on accuracy, stability and performance, while answering key questions such as: how much data is needed? how to handle noise? and how to perform control-relevant experiments? In particular, my current research interests are:
Direct data-driven control
System identification and experiment design
Kernel methods
Energy-based learning
Applications to networked systems and neuromorphic computing I am approaching the question of how much data is required for control via the concept of data informativity, see my new book and the overview paper:
The informativity approach to data-driven analysis and control To address robustness of controllers with respect to noise and bounded nonlinearities, I have worked on generalizations of the S-lemma and Finsler's lemma:
Quadratic matrix inequalities with applications to data-based control
From noisy data to feedback controllers: non-conservative design via a matrix S-lemma
A matrix Finsler's lemma with applications to data-driven control Kernel-based modeling for systems with (incremental) dissipativity properties is treated in the papers:
Towards a representer theorem for identification of passive systems
Training Lipschitz continuous operators using reproducing kernels A few works on experiment design can be found here:
A persistency of excitation condition for continuous-time systems
Beyond persistent excitation: online experiment design for data-driven modeling and control
Some recent work on energy-based learning is linked below:
A complete list of my publications and preprints can be found here or on my Google Scholar page.