A Novel Barrier Lyapunov Function-Based Online Learning Control Method for Solid Oxide Fuel Cell in DC Microgrids

This paper proposes a novel barrier Lyapunov function (BLF)-based online learning control method to enhance the performance of solid oxide fuel cells (SOFCs) in DC microgrids. Leveraging the superior function approximation capability of the radial basis function neural network (RBFNN) and employing a dual RBFNN framework, where one network approximates long-term system dynamics and the other captures rapidly changing disturbances, the proposed method achieves excellent control performance while requiring only input-output data, without any prior knowledge of the system model. The incorporation of the BLF ensures that tracking errors never exceed predefined limits at any time. By precisely regulating the output of SOFC, the proposed control method ensures a stable voltage level in the DC microgrid, thus effectively mitigating fluctuations that may affect system performance and improving the overall reliability and efficiency of the microgrid. The superior performance of the proposed method is validated through hardware-in-the-loop (HIL) experiments.