ILC for networked discrete systems with random data dropouts: A switched system approach

Author

Dong Shen ; Youqing Wang

Author_Institution

Coll. of Inf. Sci. & Technol., Beijing Univ. of Chem. Technol., Beijing, China

fYear

2014

fDate

28-30 July 2014

Firstpage

8670

Lastpage

8677

Abstract

A novel approach, switched system approach, is proposed for iterative learning control problem of networked control systems with random data dropouts. The random data dropout is described as three different forms, namely, a random sequence, a binary Bernoulli random variable, and a Markov chain, respectively. The tracking error is strictly proved to converge to zero in expectation sense, mean square sense, and almost sure sense.

Keywords

Markov processes; adaptive control; discrete systems; iterative methods; learning systems; networked control systems; ILC; Markov chain; binary Bernoulli random variable; iterative learning control; networked discrete systems; random data dropout; random sequence; switched system approach; tracking error almost sure sense; tracking error expectation sense; tracking error mean square sense; Convergence; Loss measurement; Mathematical model; Networked control systems; Random variables; Switched systems; Iterative Learning Control; Networked Control System; Random Data Dropout; Switched System Approach;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2014 33rd Chinese

Conference_Location

Nanjing

Type

conf

DOI

10.1109/ChiCC.2014.6896457

Filename

6896457