DocumentCode
3206395
Title
Sampled-data iterative learning control for a class of nonlinear systems
Author
Sun, Mingxuan ; Wang, Danwei
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
fYear
1999
fDate
1999
Firstpage
338
Lastpage
343
Abstract
In this paper, a sampled-data iterative learning control (ILC) method is proposed for a class of nonlinear continuous-time systems with higher-order relative degree. The learning control does not require differentiation of tracking error. As the sampling period is set to be small enough, a sufficient condition is derived to guarantee the convergence of the learning process. This method can be applied to a more general class of nonlinear continuous-time systems that the most existing ILC methods fail to work
Keywords
convergence; iterative methods; learning systems; nonlinear control systems; sampled data systems; tracking; ILC; convergence; high-order relative degree; learning process; nonlinear continuous-time systems; sampled-data iterative learning control; tracking error differentiation; Control systems; Convergence; Error correction; Iterative methods; Manipulators; Nonlinear control systems; Nonlinear systems; Robots; Sampling methods; Sufficient conditions;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control/Intelligent Systems and Semiotics, 1999. Proceedings of the 1999 IEEE International Symposium on
Conference_Location
Cambridge, MA
ISSN
2158-9860
Print_ISBN
0-7803-5665-9
Type
conf
DOI
10.1109/ISIC.1999.796678
Filename
796678
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