Volterra-Laguerre modeling for NMPC

Mahmoodi, Sanaz and Montazeri, Allahyar and Poshtan, Javad and Jahed-Motlagh, Mohammad Reza and Poshtan, Majid (2007) Volterra-Laguerre modeling for NMPC. In: Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. IEEE, Sharjah, pp. 1318-1321. ISBN 978-1-4244-0778-1

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Abstract

Volterra series are perhaps the best understood nonlinear system representations in signal processing. They can be used to model a wide class of nonlinear systems. However, since these models are non-parsimonious in parameters, the symmetric kernel parameters are used This model is used to evaluate identification of a pH-neutralization process. The aim is to use this model in nonlinear model predictive control framework. For this purpose various orders of the Laguerre filters and also Volterra kernels are tested and the results are compared in terms of the validation of these models. The results show that to have a good trade off between simplicity of the model and its corresponding fitness, the selected nonlinear Volterra model has the memory of 3 while the number of its kennel is 4. The VAF of this model is 99.63% which is completely acceptable for nonlinear model predictive control applications.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
65705
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Deposited On:
15 Jul 2013 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Mar 2020 08:46