Title :
Iterative learning control for networked stochastic systems with random measurement losses
Author :
Dong Shen ; Youqing Wang
Author_Institution :
Coll. of Inf. Sci. & Technol., Beijing Univ. of Chem. Technol., Beijing, China
fDate :
May 31 2014-June 2 2014
Abstract :
The iterative learning control (ILC) is constructed for the discrete-time stochastic systems with random measurement losses modeled by a stochastic sequence. A simple P-type update law is used and the almost sure convergence is strictly proved for both linear case and nonlinear case based on stochastic approximation. Illustrative examples show the effectiveness of the proposed approach.
Keywords :
convergence of numerical methods; discrete time systems; iterative methods; learning systems; nonlinear control systems; stochastic systems; P-type update law; almost sure convergence; discrete-time systems; iterative learning control; linear case; networked stochastic systems; nonlinear case; random measurement losses; stochastic approximation; stochastic sequence; Convergence; Mathematical model; Noise; Packet loss; Stochastic processes; Stochastic systems; Iterative Learning Control; Networked Stochastic System; Random Packet Losses; Stochastic Approximation;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6853065
