Robust Adaptive Control of PEMFC Air Supply System Based on Radical Basis Function Neural Network

Abstract

This technical brief emphasizes on the control of polymer electrolyte membrane fuel cell (PEMFC) air supply system. The control objective is to improve the net power output through adjusting the oxygen excess ratio within a reasonable range. In view of the problem that the PEMFC air supply system is difficult to achieve accurate modeling and stable control, a robust adaptive controller is proposed by utilizing exact linearization and radical basis function (RBF) neural network (RBFNN) system. This controller does not need the complete structure and parameters of PEMFC system. The unmodeled dynamics of PEMFC system can be approximated by RBFNN in which the adaptive learning law can be derived based on Lyapunov theory, and the external disturbance as well as the approximation error of RBFNN can be attenuated through robust control. The stability analysis shows that the system tracking error is uniformly ultimately bounded. Finally, the effectiveness and feasibility of controller are validated by hardware-in-loop (HIL) experiment.