title: Nonlinear modeling and adaptive fuzzy control of MCFC stack
abstract: To improve availability and performance of fuel cells, the operating temperature of the molten carbonate fuel cells (MCFC) stack should be controlled within a specified range. However, most existing models of MCFC are not ready to be applied in synthesis. In the paper, a radial basis function neural networks identification model of a MCFC stack is developed based on the input-output sampled data. An adaptive fuzzy control procedure for the temperature of the MCFC stack is also developed. The parameters of the fuzzy control system are regulated by back-propagation algorithm, and the rule database of the fuzzy system is also adaptively adjusted by the nearest-neighbor-clustering algorithm. Finally using the neural networks model of MCFC stack, the simulation results of the control algorithm are presented. The results show the effectiveness of the proposed modeling and design procedures for the MCFC stack based on neural networks identification and the novel adaptive fuzzy control
keyphrases: nonlinear modeling adaptive fuzzy control MCFC stack fuel cells radial basis function neural networks identification model input-output sampled data rule database nearest-neighbor-clustering algorithm molten carbonate fuel cells stack backpropagation algorithm