Identification and Control of a fuel cell system using wavelet network-based Hammerstein models

Abstract

Inspired by a direct internal reforming molten carbonate fuel cell (DIR-MCFC) coupled with complicated nonlinear dynamics, the identification and control design of the Hammerstein model is presented. Through the sequential identification procedure, the static nonlinearity block is considered as the wavelet network which is trained and validated by the on-line learning algorithm, and the linear dynamic block is described by the state-space model in which parameters are estimated by the recursive least square algorithm. When a nonlinear inversion via the numerical interpolation technique is established, a composite control framework is used to ensure the good output regulation.