Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Nonlinear model predictive control of chemical processes with a Wiener identification approach
AU - Arefi, Mohammad Mehdi
AU - Montazeri, Allahyar
AU - Poshtan, Javad
AU - Jahed-Motlagh, Mohammad Reza
PY - 2006
Y1 - 2006
N2 - Some chemical plants such as pH neutralization process have highly nonlinear behavior. Such processes demand a powerful wiener identification approach based on neural networks for identification of the nonlinear part. In this paper, the pH neutralization process is identified with NN-based wiener identification method and two linear and nonlinear model predictive controllers with the ability of rejecting slowly varying unmeasured disturbances are applied. Simulation results show that the obtained wiener model has good capability to predict the step response of the process. Parameters of both linear and nonlinear model predictive controllers are tuned and the best obtained results are compared. For this purpose, different operating points are selected to have a wide range of operation for the nonlinear process. Simulation results show that the nonlinear controller has better performance without any overshoot in comparison with linear MPC and also less steady-state error in tracking the set -points.
AB - Some chemical plants such as pH neutralization process have highly nonlinear behavior. Such processes demand a powerful wiener identification approach based on neural networks for identification of the nonlinear part. In this paper, the pH neutralization process is identified with NN-based wiener identification method and two linear and nonlinear model predictive controllers with the ability of rejecting slowly varying unmeasured disturbances are applied. Simulation results show that the obtained wiener model has good capability to predict the step response of the process. Parameters of both linear and nonlinear model predictive controllers are tuned and the best obtained results are compared. For this purpose, different operating points are selected to have a wide range of operation for the nonlinear process. Simulation results show that the nonlinear controller has better performance without any overshoot in comparison with linear MPC and also less steady-state error in tracking the set -points.
U2 - 10.1109/ICIT.2006.372470
DO - 10.1109/ICIT.2006.372470
M3 - Conference contribution/Paper
SN - 978-1-4244-0725-5
SP - 1735
EP - 1740
BT - Industrial Technology, 2006. ICIT 2006. IEEE International Conference on
PB - IEEE
CY - New York
T2 - IEEE International Conference on Industrial Technology
Y2 - 15 December 2006 through 17 December 2006
ER -