Nonlinear model predictive control from data: a set membership approach |
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Authors: | M Canale L Fagiano MC Signorile |
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Affiliation: | 1. Dipartimento di Automatica e Informatica, Politecnico di Torino, , Corso Duca degli Abruzzi 24–10129 Torino, Italy;2. Department of Mechanical Engineering, University of California, , Santa Barbara, CA, USA |
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Abstract: | 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. |
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Keywords: | predictive control robust stability nonlinear control |
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