A Time-Varying Deep Reinforcement Model Predictive Control for DC Power Converter Systems

Today power converters, especially DC/DC converters, is of great importance in power electronics applications such as DC micro-grids (MGs). However, they have some limitation such as inability to handle constant power load (CPL) which results in instability problems in MGs. Thus, a controller with specific characters including, robustness and fast response to system dynamic is vital to address the unsteadiness. In this paper, an adaptive model prediction controller (AMPC) based on Deep Reinforcement Learning (DRL) is developed to tackle the de-stabilization problem. In the proposed AMPC controller, the controlling signal coefficient in each variable operation point is regarded as the adjustable controller parameter and adaptively designed by the learning ability of the Deep Q- Network (DQN) strategy, leading to a robust controlling approach. We have shown that our suggested smart controller for DC/DC converters feeding CPLs is robust and fast in dynamic response.