On-line Optimization of Fuzzy-Immune PID for PEMFC Temperature Control Based on RBF Neural Network

Abstract

Proton Exchange Membrane Fuel Cell (PEMFC) temperature exists complex nonlinearity and is deeply disturbed by load change. Considering the characteristics of PEMFC temperature control, an improved fuzzy-immune PID algorithm is derived based on the immune feedback regulating law. Compared with general fuzzy-immune PID algorithm, radial basis function (RBF) neural network is introduced to the on-line optimization work of fuzzy-immune PID parameters, which optimizes the PID parameters on-line. Simulation results show that the proposed method in this study achieves good performance in temperature control and is useful for wide application of PEMFC.