11 citations · 25 across the 7 of their papers we have counts for
5 papers · 1 filter
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
Jilles van Hulst, Maurice Poot, Dragan Kostić +3
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-dri…
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Leontine Aarnoudse, Johan Kon, Koen Classens +5
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. Th…
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
Bram Grooten, Jelle Wemmenhove, Maurice Poot +1
In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark. Our research sugge…
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Max van Haren, Maurice Poot, Dragan Kostić +3
Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be negle…
Position-Dependent Snap Feedforward: A Gaussian Process Framework
Max van Haren, Maurice Poot, Jim Portegies +1
Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e. the compliance, should be comp…