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 - Adaptive Volterra-Laguerre modeling for NMPC
AU - Montazeri, Allahyar
AU - Mahmoodi, Sanaz
AU - Poshtan, Javad
AU - Poshtan, Majid
AU - Jahed-Motlagh, MohammadReza
PY - 2007
Y1 - 2007
N2 - Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.
AB - Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.
KW - Predictive control
KW - Identification
U2 - 10.1109/ISSPA.2007.4555605
DO - 10.1109/ISSPA.2007.4555605
M3 - Conference contribution/Paper
SN - 978-1-4244-0778-1
SP - 1322
EP - 1325
BT - Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
PB - IEEE
CY - New York
T2 - 9th International Symposium on Signal Processing and its Applications
Y2 - 12 February 2007 through 15 February 2007
ER -