Multitask robot learning control

Roberta Horowitz, Perry Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we consider the problem of determining an optimal trajectory for the execution of class of robot tasks using a learning-adaptive robot control systems. A quadratic cost functional which involves the reference trajectory and the actual control efforts is optimized on-line while the robot is learning how to execute the tasks. The control-optimization scheme presented in this paper has a hierarchical structure which consists of i) a trajectory tracking controller; ii) a 'learning' algorithm which estimates the robot dynamics; and iii) a gradient flow algorithm which attempts to minimize the cost functional using the current estimate of the robot dynamics, and generates the reference trajectory for the tracking controller. The stability of the overall control-optimization system is analyzed and the system is proved to be asymptotically stable. The reference trajectory generated by the gradient flow algorithm converges to a local minimum as long as the training tasks are sufficiently rich.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherPubl by American Automatic Control Council
Pages2623-2628
Number of pages6
ISBN (Print)0780302109, 9780780302105
DOIs
StatePublished - Jan 1 1992
EventProceedings of the 1992 American Control Conference - Chicago, IL, USA
Duration: Jun 24 1992Jun 26 1992

Publication series

NameProceedings of the American Control Conference
Volume4
ISSN (Print)0743-1619

Other

OtherProceedings of the 1992 American Control Conference
CityChicago, IL, USA
Period6/24/926/26/92

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