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Proceedings Paper

A Nonlinear Learning Controller for Robotic Manipulators
Author(s): W.Thomas Miller
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Paper Abstract

A practical learning control system is described which is applicable to complex robotic systems involving multiple feedback sensors and multiple command variables during both repetitive and nonrepetitive operations. The learning algorithm utilizes the Cerebellar Model Arithmetic Computer (CMAC) neural model developed by Albus. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is then used to predict the command signals required to produce desired changes in the sensor outputs. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved learning to use video image feedback to track three dimensional task trajectories relative to objects moving on a conveyor. No a priori knowledge of the robot kinematics or of the conveyor speed or orientation relative to the robot was assumed. In all experiments, control system tracking error was found to converge after a few trials to within error limits defined by the resolution of the sensor feedback data.

Paper Details

Date Published: 27 March 1987
PDF: 8 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937756
Show Author Affiliations
W.Thomas Miller, University of New Hampshire (United States)

Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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