Cited by Lee Sonogan
Abstract by ClaudioDe PersisaPietroTesib
This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller without estimating a model of the system. Sufficient conditions are given under which this method returns a stabilizing controller with guaranteed relative error when the data used to design the controller are affected by noise. This method has low complexity as it only requires a finite number of samples of the system response to a sufficiently exciting input, and can be efficiently implemented as a semi-definite programme.
Publication: Automatica (Peer-Reviewed Journal)
Pub Date: June 2021 Doi: https://doi.org/10.1016/j.automatica.2021.109548
Keywords: Noisy Data, Linear, Quadratic Regulators
https://www.sciencedirect.com/science/article/pii/S0005109821000686 (Plenty more sections and references in this research article)