A control-oriented multivariable Hammerstein model is used to identify the strongly nonlinear dynamics of fuel cell systems that are described by nonlinear differential or differential-algebraic equations. Within the Hammerstein model framework, the static nonlinear part is constructed by a wavelet network, and the linear dynamic part is described by a discrete-time transfer function of the state-space model. For prescribed input-output patterns for high-order fuel cell systems, simulations demonstrate the accuracy of system identification using wavelet networks in the Hammerstein structure that is better than that in the neural network structure.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)