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Probabilistic approaches to model predictive control of linear systems

Author(s):

L. Möller
Conference/Journal:

Semester Thesis, FS14 (10345)
Abstract:

In this report two different approaches to stochastic model predictive control of linear systems with additive and possibly unbounded disturbance are considered. The first method is based on a finite sampling of the chance constraints where we consider both open loop control and closed loop control with an affine disturbance feedback policy. The second approach replaces the probabilistic state- and input constraints with tighten deterministic constraints on the nominal states and inputs. Recursive feasibility and convergence are guaranteed for the latter. For the purpose of comparison some numerical examples are considered and advantages as well as disadvantages of the two methods are revealed and discussed. ii

Year:

2014
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

S. Grammatico

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_4958
}
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