DocumentCode :
3296409
Title :
On robust modifications for repetitive learning control
Author :
Yan, Rui ; Xu, Jian-Xin
Author_Institution :
Inst. for Infocomm Res., Agency for Sci., Technol. & Res., Singapore, Singapore
fYear :
2009
fDate :
15-18 Dec. 2009
Firstpage :
440
Lastpage :
445
Abstract :
In this paper, we develop two algorithms to robustify repetitive learning control (RLC), which deals with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. The first robustification algorithm is to apply a projection operator to the control input signals directly. The second robustification algorithm is to add a damping term to the learning law. Both algorithms ensure the boundedness of the learning signals. The effectiveness of the proposed robust algorithms are verified through theoretical analysis and validated through a numerical example.
Keywords :
learning (artificial intelligence); nonlinear dynamical systems; robust control; uncertain systems; nonlinear dynamical systems; nonparametric uncertainties; repetitive learning control; robust modifications; robustification algorithm; Adaptive control; Algorithm design and analysis; Control systems; Convergence; Damping; Nonlinear control systems; Nonlinear dynamical systems; Programmable control; Robust control; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on
Conference_Location :
Shanghai
ISSN :
0191-2216
Print_ISBN :
978-1-4244-3871-6
Electronic_ISBN :
0191-2216
Type :
conf
DOI :
10.1109/CDC.2009.5399702
Filename :
5399702
Link To Document :
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