2021/05/04 by Ioannis Proimadis, Yorick Broens, Proimadis, Ioannis +5
Engineering · #93-06 #C.4 #FOS: Electrical engineering #I.2.9 #Iterative Learning Control Systems #J.2 #Piezoelectric Actuators and Control #Robotic Mechanisms and Dynamics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.01458
openalex publication_date 2021/05/04 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment. However, position tracking accuracy of high precision mechatronics is often limited by the presence of disturbance sources, which originate from unmodelled or unforeseen deterministic environmental effects. To negate the effects of these disturbances, a learning based feedforward controller is employed, where the underlying control policy is estimated from experimental data based on Gaussian Process regression. The proposed approach exploits the property of including prior knowledge on the expected steady state behaviour of residual dynamics in terms of kernel selection. Corresponding hyper-parameters are optimized using the maximization of the marginalized likelihood. Consequently, the learned function is employed as augmentation of the currently employed rigid body feedforward controller. The effectiveness of the augmentation is experimentally validated on a magnetically levitated planar motor stage. The results of this paper demonstrate the benefits and possibilities of machine-learning based approaches for compensation of static effects, which originate from residual dynamics, such that position tracking performance for moving-magnet planar motor actuators is improved.