Information evolutions in the linear stochastic control systems

Author

Huang, Tongyuan ; Chen, Badong

Author_Institution

Inst. of Comput. Sci. & Eng., Chongqing Inst. of Technol., Chongqing

fYear

2008

fDate

25-27 June 2008

Firstpage

3384

Lastpage

3387

Abstract

We study the evolution law of information measure in the stochastic control system. For linear Gaussian systems, we derive the exact evolution formulas for the entropy, mutual information, divergence, and the components dependent degree (CDD) of the state vectors. A simple example is used to illustrate how the feedback will influence the information evolutions. The view of information evolution seems very useful in the analysis and design of control system.

Keywords

Gaussian processes; control system analysis; control system synthesis; entropy; feedback; linear systems; stochastic systems; component dependent degree; control system analysis; control system design; entropy; feedback; information evolution; linear Gaussian system; linear stochastic control system; mutual information; state vector; Computer science; Control systems; Entropy; Feedback; Information theory; Intelligent control; Mutual information; Probability density function; Stochastic systems; Vectors; divergence; entropy; mutual information; stochastic control system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4593463

Filename

4593463