NDLI: Robot compliance control algorithm based on neural network classification and learning of robot-environment dynamic models

Content Provider	IEEE Xplore Digital Library
Author	Katic, D. Vukobratovic, M.
Copyright Year	1997
Description	Author affiliation: Robotics Dept, Mihailo Pupin Inst., Belgrade, Yugoslavia (Katic, D.)
Abstract	In this paper, a new learning control algorithm based on neural network classification of unknown dynamic environment models and neural network learning of robot dynamic model is proposed. The method classifies characteristics of environments by using multilayer perceptrons, and then determines the control parameters for compliance control using the estimated characteristics. Simultaneously, using the second neural network the compensation of robot dynamic model uncertainties is accomplished. The classification capability of neural classifier is realized by efficient online training process. It is an important feature that the process of pattern classification can work in an online manner as a part of selected compliance control algorithm. Compliant motion simulation experiments have been performed in order to verify the proposed approach.
Starting Page	2632
Ending Page	2637
File Size	689216
Page Count	6
File Format	PDF
ISBN	0780336127
DOI	10.1109/ROBOT.1997.619358
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-04-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot control Neural networks Force control Uncertainty Stability Electronic mail Pattern classification Motion control Problem-solving Ear
Content Type	Text
Resource Type	Article

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