Nonlinear identification of alstom gasifier based on wiener model

Abstract

In this work a nonlinear identification approach has been developed and implemented on Alstom gasifier with Wiener model. The linear element of the Wiener model is identified by a combined subspace state space method, which integrates MOESP (Multivariable Output-Error State Space) and N4SID (Numerical algorithms for subspace state space system identification) method in the estimation of system matrices. A single layer neural network is chosen as the nonlinearity of the model. The quadruple system matrices are identified firstly according to the given input-output sample data. Then an initial approximation of the static nonlinear part is determined with the output sequence of linear part. At last, all parameters of the wiener model are optimized by Levenberg-Marquardt algorithm, using the model parameters obtained formerly as the initial estimates. A nonlinear model of the plant at 0% load is adopted as a base model for estimation because it is the most difficult case to control among three operating conditions. The proposed model identification method was used to model Alstom gasifier with strong nonlinearity and multivariable couples, compared to a combined linear subspace identification method. The results demonstrate that the nonlinear identification proposed, which may be applied to nonlinear predictive control, behave better approximation than the linear method.