Cart (Loading....) | Create Account
Close category search window
 

Perturbation analysis of feedback-controlled stochastic flow systems

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

2 Author(s)
Haining Yu ; Dept. of Manuf. Eng., Boston Univ., MA, USA ; Cassandras, C.G.

Stochastic flow systems arise naturally or as abstractions of discrete-event systems (DESs), referred to as stochastic flow models (SFMs). In this paper, we consider such systems operating with a feedback control mechanism, building on earlier work that has studied such SFMs without any feedback. Using infinitesimal perturbation analysis, we derive gradient estimators for loss and workload related performance metrics with respect to threshold parameters used for buffer control. These estimators are shown to be unbiased. They are also shown to depend only on data observable from a sample path of the actual DES. This renders them computable in on-line environments and easily implementable for control and performance optimization purposes. In the case of linear feedback, we further show that the estimators are nonparametric. Finally, we illustrate the use of these estimators in network control by combining them with standard gradient-based stochastic optimization schemes and providing several simulation-based examples.

Published in:

Automatic Control, IEEE Transactions on  (Volume:49 ,  Issue: 8 )

Date of Publication:

Aug. 2004

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.