DocumentCode :
2841079
Title :
Combined adaptive learning control for a class of LTV systems
Author :
Guo, Yu ; Zhou, Chuan ; Chen, Qingwei
Author_Institution :
Sch. of Autom., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
3986
Lastpage :
3991
Abstract :
In this paper, a new combined adaptive iterative learning control algorithm is proposed for a class of high order linear time-varying (LTV) systems which is repeatable over a finite time interval. The structure of iterative learning control system based on model reference adaptive control scheme is given, and adaptive learning law in both time-domain and iteration-domain is designed for time-invariant and time-varying parameters by using Lyapunov stability theory. The proposed algorithm can be applied to linear systems with time-varying and time-invariant parameters simultaneously. The convergence performance and states tracking accuracy are analyzed in details. Finally the effectiveness of the proposed algorithm is demonstrated by simulations.
Keywords :
Lyapunov methods; iterative methods; learning systems; linear systems; model reference adaptive control systems; stability; time-varying systems; LTV system; Lyapunov stability theory; adaptive iterative learning control; adaptive learning control; convergence performance; high order linear time-varying system; iteration-domain; model reference adaptive control scheme; states tracking accuracy; time-domain; time-invariant parameter; time-varying parameter; Adaptive control; Control system synthesis; Control systems; Convergence; Iterative algorithms; Linear systems; Lyapunov method; Programmable control; Time domain analysis; Time varying systems; Adaptive Control; Iterative Learning Control; Linear Time-varying System;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
Type :
conf
DOI :
10.1109/CCDC.2010.5498430
Filename :
5498430
Link To Document :
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