Monday, 16 January 2023 2:00 PM CET (8:00 AM EST)
by Tom Oomen (Eindhoven University of Technology, The Netherlands)
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Do you also have a motion system that has the same error in each task? Iterative Learning Control (ILC) can achieve perfect performance for your system. A general learning framework is presented that exploits measured error signals from previous tasks. By employing very simple models, both fast and safe learning is achieved, guaranteeing a reduction of the error in each experiment. Typically, perfect performance is achieved in only five to ten iterations. A complete design framework for motion systems is provided, while at the same time touching upon the essential theoretical foundations, including non-causality of the optimal design and the connection to traditional feedback and feedforward designs. Finally, recent approaches are explored that facilitate the implementation on industrial systems, including flexibility for a large class of tasks and multivariable systems.
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 IEEE Journal of Industry Applications Best Paper Award, and recipient of a Veni and Vidi personal grant. He is Associate Editor of the IEEE Control Systems Letters (L-CSS), IFAC Mechatronics, IEEE Transactions on Control Systems Technology, and has been vice-chair for IFAC TC 4.2. He is a member of the Eindhoven Young Academy of Engineering. His research interests are in the field of data-driven modeling, learning, and control, with applications in precision mechatronics.