Dr. Efe Balta
ETH Zürich, Switzerland, Postdoctoral Researcher

Robust Optimization Methods as Feedback Learning Controllers with Applications in Advanced Manufacturing


Optimization-based control methods use feedback information to solve optimal control problems in real-time. It can be difficult to understand the behavior of the closed-loop system, particularly when there is a mismatch between the controller model and the true system. In this talk, we will discuss robust online optimization methods as feedback controllers that learn from online measurements to solve an optimization problem with the physical system. One such method is optimization-based iterative learning control (OB-ILC), which efficiently balances model information and feedback information. We will discuss the OB-ILC approach and its performance guarantees in the context of model mismatch, with applications in advanced manufacturing. Additionally, we will explore how various popular data-driven statistical learning methods can be used in similar settings. Finally, we will look at extensions and future directions leveraging digital twins for optimization and control, with applications in additive manufacturing and precision motion control.


Efe received his B.S. degree in Manufacturing Engineering from the Faculty of Mechanical Engineering at Istanbul Technical University, Turkey, in 2016. He then earned an M.S. and Ph.D. in Mechanical Engineering from the University of Michigan, Ann Arbor, in 2018 and 2021, respectively. Since June 2021, he has been a Postdoctoral Researcher with the Automatic Control Laboratory (IfA) at ETH Zürich, Switzerland. His research interests include Control Theory, Optimization, Statistical Learning, Additive Manufacturing, Robotics, Manufacturing Science, and Systems Engineering.



Meeting ID: 963 9776 3317.

Date: Friday, February 3rd. Time: 13:30.


Yildiray Yildiz, Mechanical Engineering Department, Bilkent University