Data-Driven Modelling and Control with the Koopman Operator
Centre for Intelligent Machines and REPARTI Seminar
Speaker: Steven Dahdah, Ph.D student, Ï㽶ÊÓƵ
Meeting ID: 851 5324 8016
Passcode: 007035
Abstract:
Using the Koopman operator, nonlinear systems can be expressed as infinite-dimensional linear systems. Data-driven methods can then be used to approximate a finite-dimensional Koopman operator, which is particularly useful for system identification, control, and state estimation tasks. However, approximating large Koopman operators is numerically challenging, leading to unstable Koopman operators being identified for otherwise stable systems. Presented are a selection of techniques to regularize the Koopman regression problem, including a novel H-infinity norm regularizer. The authors' open-source Koopman operator identification library, pykoop, is also presented.
Bio:
Steven Dahdah is a Ph.D. student in the department of Mechanical Engineering at Ï㽶ÊÓƵ. He is a member of the DECAR systems group, which, under the guidance of Prof. James Richard Forbes, conducts research in the dynamics, estimation, and control of aerospace and robotic systems. He received a B.Eng. in Electrical Engineering from Ï㽶ÊÓƵ in 2019. His research explores data-driven modelling and control techniques for industrial robots.
The talk and slides will be in English.
