Model-Free Control Design for Loop Heat Pipes Using Deep Deterministic Policy Gradient

Abstract

Abstract In this paper, a model-free adaptive control design for loop heat pipes (LHPs) based on the reinforcement learning (RL) method of deep deterministic policy gradient (DDPG) is presented. An LHP as a heat transport system combines complex, thermodynamic processes, which are not yet fully described in a dynamic control model over the entire LHP operating range for model-based control design. However, RL methods provide the controller with the ability to improve its control performance without a model by analyzing and rewarding the performance online. The aim of an LHP controller is to keep the LHP operating temperature as close as possible to the fixed setpoint temperature by additional heating, while the amount of heat to be transported and the temperature of the heat sink change over time. A validated numerical simulation of the LHP provides a safe, dynamic environment for the training of the learning controller. In comparison with the commonly used PI controller with a single temperature feedback, the control performance of the learning controller observing the same temperature achieves similar control results. Furthermore, multiple observations are easily incorporated into a model-free learning controller, whereby the additional feedback of further temperature measurements ensures an improved performance over the entire operating range.