Low-complexity learning of Linear Quadratic Regulators from noisy data. (June 2021)

• Record Type: Journal Article
• Authors: De Persis, Claudio; Tesi, Pietro
• Is Part Of: Automatica. Volume 128(2021)
• Publication Date: 2021-06
• Subjects: Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2021.109548
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: 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.