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Nonlinear model predictive control from data: a set membership approach
Author(s) -
Canale M.,
Fagiano L.,
Signorile M.C.
Publication year - 2012
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.2878
Subject(s) - model predictive control , convergence (economics) , nonlinear system , stability (learning theory) , set (abstract data type) , computer science , control theory (sociology) , control (management) , upper and lower bounds , mathematical optimization , nonlinear model , mathematics , artificial intelligence , machine learning , mathematical analysis , physics , quantum mechanics , economics , programming language , economic growth
SUMMARY A new approach to design a Nonlinear Model Predictive Control law that employs an approximate model, derived directly from data, is introduced. The main advantage of using such models lies in the possibility to obtain a finite computable bound on the worst‐case model error. Such a bound can be exploited to analyze the robust convergence of the system trajectories to a neighborhood of the origin. The effectiveness of the proposed approach, named Set Membership Predictive Control, is shown in a vehicle lateral stability control problem, through numerical simulations of harsh maneuvers. Copyright © 2012 John Wiley & Sons, Ltd.