Semantic Apparatus – Low-complexity learning of Linear Quadratic Regulators from noisy data

Cited by Lee Sonogan

Data Noise – Techniques to remove Noise(Binning, Regression, Clustering),  Steps of Data Cleaning - YouTube

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:

Keywords: Noisy Data, Linear, Quadratic Regulators (Plenty more sections and references in this research article)

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