Premium
Nonlinear predictive control of hot strip rolling mill
Author(s) -
Can M.,
Kouvaritakis B.,
Grimble M.,
Bulut B.
Publication year - 2003
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.823
Subject(s) - model predictive control , nonlinear system , discretization , control theory (sociology) , linearization , convergence (economics) , mathematical optimization , computer science , stability (learning theory) , computation , algebraic equation , mathematics , control (management) , algorithm , physics , quantum mechanics , artificial intelligence , mathematical analysis , machine learning , economics , economic growth
Linear Model Predictive Control (MPC) has been applied successfully to numerous industrial problems, but its various extensions to the nonlinear case have not enjoyed the same measure of success. One of the major obstacles in this development is the prohibitive online computation required to execute receding horizon minimization of the predicted cost. This paper combines recent linear techniques, which allow for significant reductions in online computational load, with Linear Difference Inclusion in order to apply MPC to a rolling mill problem described by a set of algebraic and differential/integral nonlinear equations, discretized to give a suitable time‐varying uncertain linear model. Through successive optimization of an approximate cost derived by linearization about predicted trajectories, we obtain MPC laws with guaranteed stability and convergence to a (possibly local) minimum of the performance index predicted on the basis of the full nonlinear model dynamics. The efficacy of the approach is illustrated by means of simulation results presented at the end of the paper. Copyright © 2003 John Wiley & Sons, Ltd.