11 citations · 25 across the 7 of their papers we have counts for
7 papers
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…
Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction
Max van Meer, Maurice Poot, Jim Portegies +1
Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of…
On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control
Maurice Poot, Jim Portegies, Tom Oomen
Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this…