NDLI: Neural network learning from hint for the cyclic motion of the constrained redundant arm

Content Provider	IEEE Xplore Digital Library
Author	Assal, S.F.M.
Copyright Year	2006
Description	Author affiliation: Dept. of Adv. Syst. Control Eng., Saga Univ. (Assal, S.F.M.)
Abstract	A Widrow-Hoff neural network (NN) with an online adaptive learning algorithm derived by applying Lyapunov approach is introduced for the kinematic inversion of redundant arms. The developed approach is designed to enable the manipulator to conserve the joint configuration in cyclic trajectories and to avoid the joint limits. Since the inverse kinematics in this problem has an infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. Feeding this vector as an additional hint input vector to the NN limits and guides the output of the NN within the self-motion. The derivation of the Lyapunov function which is designed to achieve both of the tasks, leads to a computationally efficient online learning algorithm of the NN. The effectiveness of the developed approach is studied by conducting experiments on the PA-10 redundant manipulator
Sponsorship	IEEE Robotics and Autom. Soc
Starting Page	3679
Ending Page	3684
File Size	422871
Page Count	6
File Format	PDF
ISBN	0780395050
ISSN	10504729
DOI	10.1109/ROBOT.2006.1642264
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-05-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Kinematics Jacobian matrices Fuzzy neural networks Control engineering Programmable control Adaptive control Arm Lyapunov method Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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