Fuel cell voltage control using neural network based on model predictive control

In this paper, a neural network based model predictive control (NNMPC) algorithm was implemented to control the voltage of a proton exchange membrane fuel cell (PEMFC). In this approach, a neural network model is trained to predict the future process response over the specified horizon. The predictions are passed to a numerical optimization routine which attempts to minimize a specified cost function to calculate a suitable control signal at each sample instant. The performance results of the NNMPC were compared with a fuzzy controller.