Multi‐Objective Robust Sliding Mode Control of Proton Exchange Membrane Fuel Cell Gas Supply System Based on Neural Network Observers for Cathode and Anode Pressures

ABSTRACT The flow rates of the air and hydrogen and the gas pressures of the cathode and anode have a significant effect on the performance of the proton exchange membrane fuel cell (PEMFC). In order to improve the robustness of the PEMFC gas supply control system and thus its ability to regulate the gas flow rates and the gas pressures, firstly, two observers, whose error feedback terms are constructed using a neural network, are designed to estimate the gas pressures within the stack and thus to estimate the excess ratio of oxygen and hydrogen. Secondly, four extended observers are used for the feedback linearization of the gas supply system, and the sliding mode control laws based on the Hamilton‐Jacobi inequality are designed for fast and accurate adjustment of the air and hydrogen flow rates supplied to the stack and the gas pressures of the two poles within the stack. Simulation results indicate that the neural network observer has a stronger pressure estimation capability and noise rejection ability than the linear extended observer, and the proposed controllers have minimal overshoot and settling times for the regulation of air and hydrogen flow rates and pressures compared to the proportional‐integral‐derivative controllers or the feedback linearization controllers.