DocumentCode :
2975708
Title :
Internal model-based robust iterative learning control for uncertain LTI systems
Author :
Tayebi, Abdelhamid ; Zaremba, Marek B.
Author_Institution :
Dept. of Electr. Eng., Lakehead Univ., Thunder Bay, Ont., Canada
Volume :
4
fYear :
2000
fDate :
2000
Firstpage :
3439
Abstract :
Investigates the combination of an iterative learning control (ILC) with an internal model control (IMC) for uncertain linear time-invariant (LTI) systems. The convergence of the iterative process is investigated and reformulated as a general robust control problem. For a certain choice of the IMC and ILC filters, we prove that the condition of convergence to zero of the iterative process is nothing but the robust performance condition of the IMC structure. Using the general robust control formulation, we propose a design procedure for the ILC-IMC filters using the μ-synthesis approach
Keywords :
convergence; filtering theory; learning systems; linear systems; model reference adaptive control systems; robust control; uncertain systems; μ-synthesis approach; design procedure; internal model-based robust iterative learning control; linear time-invariant systems; robust performance condition; uncertain LTI systems; Automatic control; Automatic generation control; Control systems; Convergence; Filters; Intelligent robots; Iterative methods; Open loop systems; Robust control; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
ISSN :
0191-2216
Print_ISBN :
0-7803-6638-7
Type :
conf
DOI :
10.1109/CDC.2000.912235
Filename :
912235
Link To Document :
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