Nonlinear model predictive control from data: a set membership approach
- 24 July 2012
- journal article
- research article
- Published by Wiley in International Journal of Robust and Nonlinear Control
- Vol. 24 (1) , 123-139
Abstract
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.Keywords
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