Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines

This paper deals with Wiener model based predictive control ofa pH neutralization process. The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines （LSSVM）. Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification. Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression （SVR） Wiener models, Laguerre-polynomial Wiener models, and linear Laguerre models. The identified Wiener model is used here for nonlinear model predictive control （NMPC） of the pH neutralization process. The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC, Laguerre-polynomial Wiener model based NMPC, and linear model predictive control （LMPC）. Validation results show that the proposed NMPC outperforms the other three controllers.