Adaptive Inverse Control of Proton Exchange Membrane Fuel Cell Using RBF Neural Network

Proton exchange membrane fuel cells (PEMFCs) present remarkable control demands, due to the inherent nonlinear characteristics and time-varying parameters. This paper deals with the application of adaptive inverse control using radial basis function neural network (RBFNN) to PEMFC system. This control scheme has the advantage of not needing to identify the dynamical parameters of the system for design and scheduling of the controller parameters. In order to improve the control aim and to guarantee the closed loop stability as well as system's robustness a feedback PD controller is combined with the RBF-based adaptive inverse control. Results from the simulation manifest that the inverse control scheme is promising technique and can ensure the satisfactory performance.