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Indirect decentralized repetitive controlLearning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.
Document ID
19950049751
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Lee, Soo Cheol
(Columbia Univ. New York, NY, US, United States)
Longman, Richard W.
(Columbia Univ. New York, NY, US, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1993
Publication Information
Publication: In: Spaceflight mechanics, 1993; AAS(AIAA Spaceflight Mechanics Meeting, 3rd, Pasadena, CA, Feb. 22-24, 1993, Parts 1 & 2 . A95-81344
Publisher: American Astronautical Society (Advances in the Astronautical Sciences, Vol. 82, Pts. 1 & 2)
ISSN: 0065-3438
Subject Category
Cybernetics
Accession Number
95A81350
Funding Number(s)
CONTRACT_GRANT: NAG1-649
Distribution Limits
Public
Copyright
Other

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