Low-complexity learning of Linear Quadratic Regulators from noisy data
نویسندگان
چکیده
This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central in data-driven control and reinforcement learning. We propose method that uses data to directly return controller without estimating model of system. Sufficient conditions are given under which this returns stabilizing guaranteed relative error when used design affected by noise. has low complexity as it only requires finite number samples system response sufficiently exciting input, can be efficiently implemented semi-definite programme.
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ژورنال
عنوان ژورنال: Automatica
سال: 2021
ISSN: ['1873-2836', '0005-1098']
DOI: https://doi.org/10.1016/j.automatica.2021.109548