Research Profile
We make use of model predictive control (MPC) theory to deal with constraints and we design MPC algorithms and fast MPC solvers for complex systems (highly nonlinear, hybrid, uncertain or large-scale interconnected systems). We research flexible control Lyapunov functions to enforce stability for real-time controllers. To increase autonomy and reliability of control systems we focus on integration of artificial intelligence (neural networks) with classical and predictive controllers.
Read moreProjects
Meet some of our Researchers
Most important active projects
- HTSM TTW-NWO grant: Embedded power electronics, converters and control (in collaboration with Prof. Elena Lomonova, EPE, Electrical Engineering, TU/e)
- EU H2020 project: Implementation of powertrain control for economic, low real driving emissions and fuel consumption (Imperium, in collaboration with Dr. John Kessels, DAF)
Recent Publications
Our most recent peer reviewed publications
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Physics-guided neural networks for feedforward control with input-to-state-stability guarantees
Control Engineering Practice (2024) -
Data-driven feedforward control design for nonlinear systems
(2024) -
Nonlinear Data-Driven Predictive Control Using Deep Subspace Prediction Networks
(2024) -
Handbook of linear data-driven predictive control
Annual Reviews in Control (2023) -
Practical deadbeat MPC design via controller matching with applications in power electronics
European Journal of Control (2023)
Contact
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Visiting address
FluxGroene Loper 195612 AP EindhovenNetherlands -
Visiting address
FluxGroene Loper 195612 AP EindhovenNetherlands -
Postal address
P.O. Box 513Department of Electrical Engineering5600 MB EindhovenNetherlands -
Postal address
P.O. Box 513Department of Electrical Engineering5600 MB EindhovenNetherlands