In this work, a fast nonlinear model-based predictive control (NMPC) strategy is designed and experimentally validated on-line on a real fuel cell. Regarding NMPC strategies, the most challenging part remains to achieve on-line implementation, especially when dealing with fast dynamic systems. As previously demonstrated in a recent work, the proposed control strategy is ideally suited to address this problem. Indeed, it is 30 times faster than classical NMPC controllers. This strategy relies on a specific parameterization of the control actions to reduce the computational time and achieve on-line implementation. Due to its short computational time compared to mechanistic models, an artificial neural network model is designed and experimentally validated. This model is employed as internal model in the NMPC controller to predict the system behavior. To confirm the applicability and the relevance of the proposed NMPC controller varying control scenarios are investigated on a test bench. The built-in controller is overridden and the NMPC controller is implemented externally and executed on-line. Experimental results exhibit the outstanding tracking capability and robustness against model-process mismatch of the proposed strategy. The parameterized NMPC controller turns out to be an excellent candidate for on-line applications.