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Nonlinear predictive control of smooth nonlinear systems based on Volterra models. Application to a pilot plant
Author(s) -
Gruber Jorn K.,
Bordons Carlos,
Bars Ruth,
Haber Robert
Publication year - 2009
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1549
Subject(s) - nonlinear system , model predictive control , autoregressive model , control theory (sociology) , volterra series , computer science , range (aeronautics) , process (computing) , control (management) , mathematical optimization , mathematics , engineering , artificial intelligence , statistics , physics , quantum mechanics , aerospace engineering , operating system
There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non‐autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real‐time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd.