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Strongly Feasible Stochastic Model Predictive Control

Author(s):

M. Korda, R. Gondhalekar, J. Cigler, F. Oldewurtel
Conference/Journal:

Conference on Decision and Control (CDC)
Abstract:

In this article we develop a systematic approach to ensure strong feasibility of stochastic model predictive control problems under ane disturbance feedback policies. Two distinct approaches are presented, both of which capitalize and extend the machinery of controlled invariant sets to a stochastic environment. The first approach employs an invariant set as a terminal constraint, whereas the second one constrains the first predicted state. Consequently, the second approach turns out to be completely independent of the policy in question and moreover it produces the largest feasible set amongst all admissible policies. As a result a trade-o between computational complexity and performance can be found without compromising feasibility properties. Our results are demonstrated by means of two numerical examples.

Year:

2011
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { KorEtal:2011:IFA_3842,
    author={M. Korda and R. Gondhalekar and J. Cigler and F. Oldewurtel},
    title={{Strongly Feasible Stochastic Model Predictive Control}},
    booktitle={Conference on Decision and Control (CDC)},
    pages={},
    year={2011},
    address={},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3842}
}
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