DocumentCode :
724445
Title :
Almost sure and mean square convergence of ILC for linear systems with randomly varying iteration lengths
Author :
Dong Shen ; Wei Zhang ; Youqing Wang ; Chiang-Ju Chien
Author_Institution :
Coll. of Inf. Sci. & Technol., Beijing Univ. of Chem. Technol., Beijing, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
4546
Lastpage :
4551
Abstract :
This note proposes convergence analysis for iterative learning control (ILC) design problem for discrete-time linear systems with randomly varying iteration length. No prior information on the probability distribution of stochastic iteration length is required. The simple P-type update algorithm is adopted in this note with Arimoto-like gain and/or causal gain. A novel switching system approach is introduced to prove the convergence both in almost sure and mean square senses. Further extensions to linear time-varying systems, randomly initial state condition, and stochastic linear systems are also provided.
Keywords :
discrete time systems; iterative learning control; linear systems; statistical distributions; stochastic processes; switching systems (control); Arimoto-like gain; ILC design; P-type update algorithm; almost sure convergence; discrete-time linear system; iterative learning control; linear time-varying system; mean square convergence; probability distribution; stochastic iteration length; switching system approach; Algorithm design and analysis; Convergence; Linear systems; Switching systems; Time-varying systems; Trajectory; Almost Sure Convergence; Iterative Learning Control; Mean Square Convergence; Non-uniform Iteration Length;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162726
Filename :
7162726
Link To Document :
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