DocumentCode
2404629
Title
Iterative learning control-convergence using high gain feedback
Author
Owens, David H.
Author_Institution
Centre for Syst. & Control Eng., Exeter Univ., UK
fYear
1992
fDate
1992
Firstpage
2545
Abstract
The author presents a convergence theory for iterative learning control based on the use of high-gain current trial feedback for the special case of relative degree one, MIMO (multiple-input multiple-output) minimum-phase systems. The results are related to those of Padieu and Su (1990) via the notion of positive real systems. In particular, positive real systems are easily arranged to have convergent learning by simple proportional learning rules of arbitrary positive gain
Keywords
convergence; feedback; iterative methods; learning systems; MIMO; convergence theory; convergent learning; high gain feedback; iterative learning control; minimum-phase systems; positive real systems; Control engineering; Control systems; Convergence; Error correction; Feedback; Iterative algorithms; MIMO; Robots; Signal generators; Stability; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location
Tucson, AZ
Print_ISBN
0-7803-0872-7
Type
conf
DOI
10.1109/CDC.1992.371067
Filename
371067
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