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Robust Model Predictive Control of Nonlinear Systems with Bounded and State-Dependent Uncertainties

Author(s):

Gilberto Pin, D. M. Raimondo, Lalo Magni, Thomas Parisini
Conference/Journal:

IEEE Transactions on Automatic Control, vol. 54, no. 7, pp. 1681-1687
Abstract:

In this note, a robust model predictive control scheme for constrained discrete-time nonlinear systems affected by bounded disturbances and state-dependent uncertainties is presented. In order to guarantee the robust satisfaction of the state constraints, restricted constraint sets are introduced in the optimization problem, by exploiting the state-dependent nature of the considered class of uncertainties. Moreover, unlike the nominal model predictive control algorithm, a stabilizing state constraint is imposed at the end of the control horizon in place of the usual terminal constraint posed at the end of the prediction horizon. The regional input-to-state stability of the closed-loop system is analyzed. A simulation example shows the effectiveness of the proposed approach.

Year:

2009
Type of Publication:

(01)Article
Supervisor:

M. Morari

No Files for download available.
% Autogenerated BibTeX entry
@Article { PinEtal:2009:IFA_3288,
    author={Gilberto Pin and D. M. Raimondo and Lalo Magni and Thomas Parisini},
    title={{Robust Model Predictive Control of Nonlinear Systems with
	  Bounded and State-Dependent Uncertainties}},
    journal={IEEE Transactions on Automatic Control},
    year={2009},
    volume={54},
    number={7},
    pages={1681--1687},
    month=jul,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3288}
}
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