Distributionally Robust Linear Quadratic Control
Speaker: Çağıl Koçyiğit
LCL, Université du Luxembourg
DATE: Tuesday, 10 October 2023
TIME: 13.00 – 14.00
LANGUAGE: English
Location:
Campus Kirchberg
6, Rue Richard Coudenhove-Kalergi
L-1359 Luxembourg
Registration:
– Free seminar
– Registration to dem@uni.lu (please specify full name and institution)
Contact:
dem@uni.lu
Tel: +352 46 66 44 6283
Abstract:
Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations, subject to additive noise, with the goal of minimizing a quadratic cost function for the state and control variables. In this work, we consider a generalization of the discrete-time, finite-horizon LQG problem, where the noise distributions are unknown and belong to Wasserstein ambiguity sets centered at nominal (Gaussian) distributions. The objective is to minimize a worst-case cost across all distributions in the ambiguity set, including non-Gaussian distributions. Despite the added complexity, we prove that a control policy that is linear in the observations is optimal for this problem, as in the classic LQG problem. We propose a numerical solution method that efficiently characterizes this optimal control policy. Our method uses the Frank-Wolfe algorithm to identify the least-favorable distributions within the Wasserstein ambiguity sets and computes the controller’s optimal policy using Kalman filter estimation under these distributions.