Control of a direct internal reforming molten carbonate fuel cell system using wavelet network-based Hammerstein models

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. Using the numerical interpolation technique to approximate the implicit nonlinear function, we present a composite control framework consists of a nonlinear inversion and linear control. Through the closed-loop simulation tests, the nonlinear inversion design for the nonlinearity cancellation of a class of nonlinear systems is validated.