ICONS 2019 Paper Abstract


Paper ThB2SP.4

Hauser, Jan (Czech Technical University in Prague), Pachner, Daniel (Honeywell), Havlena, Vladimir (Honeywell Intl.)

Gaussian Process Based Model-Free Control with Q-Learning

Scheduled for presentation during the Regular Session "Learning and Control" (ThB2SP), Thursday, August 22, 2019, 17:00−17:20,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on November 29, 2021

Keywords Learning, adaptation and evaluation, Nonlinear control and its application, Reinforcement learning


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 sub-optimal controller. A simpli ed nonlinear Fan Coil Unit (FCU) model is used as an example for which the fan speed control is designed using the o -policy Q-learning algorithm. Additionally, the algorithm properties are discussed, i.e. learning process robustness, GP kernel functions choice. The simulation results are compared to a simple PI, designed based on a linearized model.


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