From Data to Advanced Motion Control

Abstract
The future of manufacturing equipment and scientific instruments hinges on the ability to perform precise and fast motions. Examples of such mechatronic systems include wafer scanners, printing systems, pick-and-place machines, microscopes, and telescopes. These systems are subject to ever-increasing speed, accuracy, and flexibility requirements. Learning from data provides major opportunities to meet these requirements. In this presentation, I will outline new identification, learning, and control methodologies that can deal with the increasing requirements and large complexity in envisaged future mechatronic systems. I will also show their  successful implementation on a selection of state-of-the-art mechatronic systems. The new results pave the way for new, revolutionary, data-intensive mechatronic designs with a massive number of actuators and sensors. 

Bio
Tom Oomen is full professor with the Department of Mechanical Engineering at the Eindhoven University of Technology. He is also a part-time full professor with the Delft University of Technology. He received the M.Sc. degree (cum laude) and Ph.D. degree from the Eindhoven University of Technology, Eindhoven, The Netherlands. He held visiting positions at KTH, Stockholm, Sweden, and at The University of Newcastle, Australia. He is a recipient of the 7th Grand Nagamori Award, the Corus Young Talent Graduation Award, the IFAC 2019 TC 4.2 Mechatronics Young Research Award, the 2015 IEEE Transactions on Control Systems Technology Outstanding Paper Award, the 2017 IFAC Mechatronics Best Paper Award, the 2019 IEEJ Journal of Industry Applications Best Paper Award, and recipient of a Veni and Vidi personal grant. He is currently a Senior Editor of IEEE Control Systems Letters (L-CSS) and Co-Editor-in-Chief of IFAC Mechatronics, and he has served on the editorial board of IEEE Transactions on Control Systems Technology. He is also vice-chair for IFAC TC 4.2. His research interests are in the field of data-driven modeling, learning, and control, with applications in precision mechatronics.