A Novel Neural Network-Based Control Method for Proton Exchange Membrane Fuel Cell in DC Microgrids

In this article, we propose a novel neural network-based control algorithm for the proton exchange membrane fuel cell in dc microgrids. A novel recurrent equilibrium network (REN) is developed to improve the control performance in the presence of uncertainties and disturbances. In theory, the contraction of the REN is guaranteed, regardless of the inputs or initial conditions. Furthermore, the REN is directly parameterized, thereby simplifying the neural network training process without sacrificing learning performance. These features make REN particularly suitable for machine learning and system identification tasks. In summary, the proposed REN-based proportional-integral (PI) control method has very low computational complexity, excellent control performance, and verified stability. The effectiveness of the proposed control algorithm is validated through hardware-in-the-loop experiments.