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Feed-forward and feedback control is developed in this work for Polymer electrolyte membrane (PEM) fuel cell stacks. The feed-forward control is achieved using different methods, including look-up table, fuzzy logic and neural network, to improve the fuel cell stack breathing control and prevent the problem of oxygen starvation. Firstly, the feed-forward controller is used to generate directly an input voltage of the compressor according to the current demand. Then, a PID controller is used in the feedback to adjust the difference between the requested and the actual oxygen ratio by compensating the feed-forward controller output. The designed system is evaluated using a nonlinear simulation of a fuel cell model documented in the literature. The proposed feed-forward with PID controller have achieved a good control performance. The simulation showed effectiveness of the control strategy.

Robust control for nonlinear uncertain systems has been solved for matched uncertainty but has not been completely solved yet for unmatched uncertainty. This paper developed a new method in which an adaptive radial basis function neural network is used to compensate for the effects of unmatched uncertainty in the framework of integral sliding mode control. The stability of the whole system is guaranteed by the Lyapunov method. The adaptation algorithm of the network is also derived by the Lyapunov function so that its convergence is also guaranteed. A numerical example is used to show the effectiveness of the proposed method. Improvement against existing methods is also demonstrated.