2-216 Artificial Neural Network Controller for Forcefree Control of Articulated Robot Arm
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概要
- 論文の詳細を見る
This paper presents a design method of artificial neural network controller for realizing forcefree control of articulated robot arm. The forcefree control enables a motion of articulated robot arm as if it were in circumstance of non-gravity and non-friction in order to improve the flexibility of rigid robot arm. Since the original proposed forcefree control requires rigorous model of articulated robot arm, the influences of unknown factors, such as friction coefficient, interference between robot links, error from the torque monitor, etc, always cause the deterioration of control performance. In this paper, an artificial neural network controller is constructed by Gaussian neural network. With proper selection of initial parameters of neural network by the model of articulated robot arm as well as learning from the actual data, the neural network controller can grasp the inverse dynamic of articulated robot arm with many unknown factors accurately and the parameters of neural network can be easily converged. Through the control by this neural network controller, the influences of the unknown factors can be reduced remarkably and hence the robot arm can move freely by the external forces. Therefore, many kinds of motion of rigid robot arm for various tasks, such as direct teaching, pull-out work, etc, can be implemented easily. The experiment and simulation results verified the significant potential of the proposed method for articulated robot arm.
- 一般社団法人日本機械学会の論文
- 2002-11-14
著者
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ZHANG Tao
Department of Chemistry, Faculty of Engineering, Mie University
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Nakamura Masatoshi
Department of Pediatric Surgery, Reproductive and Developmental Medicine, Graduate School of Medical
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Zhang Tao
Department Of Advanced Systems Control Engineering Graduate School Of Science And Engineering Saga U
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Nakamura Masatoshi
Department Of Advanced Systems Control Engineering Graduate School Of Science And Engineering Saga U
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