Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

On convex problems in chance-constrained stochastic model predictive control

Author(s):

E. Cinquemani, M. Agarwal, D. Chatterjee, J. Lygeros
Conference/Journal:

http://arxiv.org/abs/0905.3447
Abstract:

We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost over a finite horizon. Hard constraints are introduced first, and then reformulated in terms of probabilistic constraints. It is shown that, for a suitable parametrization of the control policy, a wide class of the resulting optimization problems are convex, or admit reasonable convex approximations.

Year:

2011
Type of Publication:

(04)Technical Report
Supervisor:

J. Lygeros

File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@TechReport { CinEtal:2011:IFA_3317,
    author={E. Cinquemani and M. Agarwal and D. Chatterjee and J. Lygeros},
    title={{On convex problems in chance-constrained stochastic model
	  predictive control}},
    institution={},
    year={2011},
    number={},
    address={},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3317}
}
Permanent link