In my research, I focus on learning from data for feedforward control of dynamic systems. Within feedforward control, both high performance as well as task flexibility are required. While model-based approaches are robust against varying tasks, their performance is limited by unmodelled dynamics that does not match the model. Learning control methods, such as iterative learning control, often lead to superior performance, but are only applicable to a single reference. My research focuses on learning and compensating the unmodelled effects through complementing model-based approaches with universal function approximators from the field of machine learning. Interpretability, data-efficiency, and safety are key topics within this research. The research is supported by and validated on industrial motion systems at ASML and Philips.
Johan Kon received his BSc Mechanical Engineering (cum laude) and MSc Systems and Control (cum laude) degree from Eindhoven University of Technology, Eindhoven, The Netherlands. BSc en MSc (cum laude). As part of his studies, he did an internship on model-free reinforcement learning at Sapienza University of Rome, and graduated in collaboration with Canon Production Printing on learning control.
Unifying model-based and neural network feedforward61st IEEE Conference on Decision and Control, CDC 2022 (2023)
Cross-coupled iterative learning control for complex systems(2023)
Data-driven compensation of unmodeled dynamics for complex mechatronic systems(2022)
Data-driven compensation of unmodeled dynamics for complex mechatronic systems2nd Euspen Special Interest Group Meeting on Precision Motion Systems & Control (2022)
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach2022 American Control Conference, ACC 2022 (2022)
Current Educational Activities
- Begeleiden van cursussen georganiseerd door Mechatronics Academy, Mechatronics Academy