Nonlinear model predictive control from data: a set membership approach
Article first published online: 24 JUL 2012
Copyright © 2012 John Wiley & Sons, Ltd.
International Journal of Robust and Nonlinear Control
Volume 24, Issue 1, pages 123–139, 10 January 2014
How to Cite
Canale, M., Fagiano, L. and Signorile, M.C. (2014), Nonlinear model predictive control from data: a set membership approach. Int. J. Robust Nonlinear Control, 24: 123–139. doi: 10.1002/rnc.2878
- Issue published online: 10 DEC 2013
- Article first published online: 24 JUL 2012
- Manuscript Accepted: 25 JUN 2012
- Manuscript Revised: 5 JUN 2012
- Manuscript Received: 22 MAR 2011
- predictive control;
- robust stability;
- nonlinear control
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.