Gaussian Process Based Model-free Control with Q-Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335629" target="_blank" >RIV/68407700:21230/19:00335629 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ifacol.2019.09.147" target="_blank" >https://doi.org/10.1016/j.ifacol.2019.09.147</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2019.09.147" target="_blank" >10.1016/j.ifacol.2019.09.147</a>

Result language
angličtina
Original language name
Gaussian Process Based Model-free Control with Q-Learning
Original language description
The aim of this paper is to demonstrate a new algorithm for Machine Learning (ML) based on Gaussian Process Regression (GPR) and how it can be used as a practical control design technique. An optimized control law for a nonlinear process is found directly by training the algorithm on noisy data collected from the process when controlled by a suboptimal controller. A simplified nonlinear Fan Coil Unit (FCU) model is used as an example for which the fan speed control is designed using the off-policy Q -learning algorithm. Additionally, the algorithm properties are discussed, i.e. learning process robustness, Gaussian Process (GP) kernel functions choice. The simulation results are compared to a simple PI design based on a linearized model. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Czech name
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Czech description
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Project
<a href="/en/project/GA18-26278S" target="_blank" >GA18-26278S: Incorporation of Prior Knowledge for Identification of Nonlinear Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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