Control approaches based on artificial intelligence are hardly used in power conversion. This can be partially explained by the fact that neural network decisions can often not be understood by humans. Interpretable AI models provide transparency, but they are harder to design: based on a neural network mimicking PID regulation several extensions based on typical neural network techniques are proposed. This way, the algorithm stays comprehensible independent of the parameter values. Then, a reinforcement learning approach is proposed and first simulation results of this new class of regulation schemes are provided for a single phase buck converter.
