Event-Driven Based Reinforcement Learning Predictive Controller Design for Three-Phase NPC Converters Using Online Approximators

This article is concerned with a two-step event-driven in reinforcement learning model-free predictive control problem leveraging online approximators for power converter systems, in which the limitations from system uncertainties and unnecessary switching loss are all addressed. To be specific, the key features of this technical note are: 1) a critic neural network to learn the performance function in real-time; 2) an actor neural network to approximate the predictive controller online and to minimize the learned performance function obtained from the critic network; 3) a two-step event-driven control protocol to attenuate the switching frequency (SF). Also, we further discuss the sensitivity of the proposal to parametric uncertainties and quantify its performance under low SF operation and unknown disturbances conditions. Further, the convergence analysis of the networks' weight estimation errors is manifested. Finally, we evaluate the suggested controller by means of various numerical examples, and the results found are promising and motivate further research in this field.