By Topic

Robust Stabilization of linear stochastic differential models with additive and multiplicative diffusion via attractive ellipsoid techniques

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lozada-Castillo, N.B. ; Dept. of Autom. Control, CINVESTAV-IPN, Mexico City, Mexico ; Alazki, H. ; Poznyak, A.S.

Linear controlled stochastic differential equations (LCSDE) subject to both multiplicative and additive stochastic noises are considered. We study a robust “practical” stabilization for this class of LCSDE meaning that almost all trajectories of this stochastic model converges in a “mean-square sense” to a bounded zone located in an ellipsoidal set. Also, we present a result related to convergence in probability one sense to a zero zone. The considered stabilizing feedback is supposed to be linear. This problem is shown to be converted into the corresponding attractive averaged ellipsoid “minimization” under some constraints of BMI's (Bilinear Matrix Inequalities) type. The application of an adequate coordinate changing transforms these BMI's into a set of LMI's (Linear Matrix Inequalities) that permits to use directly the standard MATLAB - toolbox. A numerical example is used to illustrate the effectiveness of this methodology.

Published in:

Electrical Engineering Computing Science and Automatic Control (CCE), 2011 8th International Conference on

Date of Conference:

26-28 Oct. 2011