Nonlinear model predictive control using a Wiener model of a continuous methyl methacrylate polymerization reactor

A subspace-based identification method of the Wiener model, consisting of a state-space linear dynamic block and a polynomial static nonlinearity at t he output, is used to retrieve the accurate information about the nonlinear dynamics of a polymerization reactor from the input-output data. The Wiene r model may be incorporated into model predictive control (MPC) schemes in a unique way that effectively removes the nonlinearity from the control pro blem, preserving many of the favorable properties of the linear MPC. The co ntrol performance is evaluated by simulation studies, for which the origina l first-principles model for a continuous methyl methacrylate polymerizatio n reactor takes the role of the plant while the identified Wiener model is used for control purposes. On the basis of the simulation results, it is de monstrated that, under the presence of strong nonlinearities, the Wiener mo del predictive controller (WMPC) performed quite satisfactorily for the con trol of polymer qualities in a continuous polymerization reactor. The WMPC strategy proposed is validated by conducting an online digital control expe riment with an online densitometer and viscometer. It is observed that the WMPC performs satisfactorily for the polymer property control of the highly nonlinear multiple-input multiple-output system with input constraints.