Gaussian Process Based Model-free Control with Q-Learning

Abstract

Abstract 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 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.