SE Seminar Series

Speaker: Caio Kalil Lauand

Title: Online Feedback Optimization with Random System Response: Theory and Applications to Power Systems

Abstract: In several applications of online optimization to networked systems such as power grids and robotic networks, information about the system model and its disturbances is not generally available. The framework of online feedback optimization (OFO) addresses these challenges by leveraging real-time input-output measurements to drive optimization algorithms online. This talk covers approaches to OFO when there is randomness in the system's response or actuation, arising either from agents that do not comply with their controllers, or from random parameters whose distribution shifts in response to the control actions. In both settings, we establish mean-square tracking guarantees that decompose the effects of randomness, measurement error, and time-variability of the problem, and illustrate the theory on power systems case studies.

Bio: Caio Kalil Lauand received the B.S. degree in electrical engineering from the University of North Florida in 2019, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Florida in 2023 and 2025, respectively, under the supervision of Prof. Sean Meyn. He is now a postdoctoral associate in the Division of Systems Engineering at Boston University, working with Profs. Emiliano Dall'Anese and Alexander Olshevsky. His research interests include stochastic approximation and its applications, such as optimization and reinforcement learning.