DocumentCode
1662388
Title
Minimum entropy filtering for networked control systems via information theoretic learning approach
Author
Zhang, Jianhua ; Cai, Lei ; Wang, Hong
Author_Institution
Beijing Key Lab. of Ind. Process Meas. & Control New Technol. & Syst., North China Electr. Power Univ., Beijing, China
fYear
2010
Firstpage
774
Lastpage
778
Abstract
In this paper, a minimum entropy filter is presented for estimating states in networked control systems with multiple-packet transmission mechanism and non-Gaussian time-delay and noises. The filter is designed for nonlinear NCSs via information theoretic learning approach based on stochastic gradient algorithm. A numerical example is given to illustrate the effectiveness of the proposed scheme.
Keywords
Gaussian processes; filtering theory; gradient methods; minimum entropy methods; nonlinear control systems; stochastic systems; information theoretic learning approach; minimum entropy filtering; multiple-packet transmission mechanism; networked control systems; nonGaussian time-delay; nonlinear NCS; stochastic gradient algorithm; Noise; Radio access networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Modelling, Identification and Control (ICMIC), The 2010 International Conference on
Conference_Location
Okayama
Print_ISBN
978-1-4244-8381-5
Electronic_ISBN
978-0-9555293-3-7
Type
conf
Filename
5553464
Link To Document