In this paper, a nonlinear model predictive control (NMPC) based on a piecewise linear Wiener model is applied to a polymerization reactor. The static nonlinear part of the applied Wiener model is approximated using the piecewise linear functions and its dynamic linear element is identified using a state-space description. Due to the nonlinear gain of model, for gathering data, a generalized multiple-level noise (GMN) test has been used. This test demonstrates the response of the system to a range of amplitude changes. The predictive control based on this model retains all the interested properties of the classical linear MPC. This approach leads to a quadratic programming problem due to the canonical structure of the nonlinear gain. The control scheme has been applied to a polymerization reactor as a MIMO process. Results show that the used Wiener model is able to identify the nonlinear processes effectively. The nonlinear predictive control based on this model is compared to the linear MPC. The parameters of both linear and nonlinear model predictive controllers are tuned and the performances of both methods are compared. It is shown that the nonlinear controller has a better performance, having short settling time and without any overshoot compared to its linear one. Moreover, this controller has a good performance and rejects unmeasured disturbances effectively.
