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Back-propagation neural networks for identification and control of a direct drive robot

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Abstract

A neural approach is proposed to estimate parameters in dynamics of a direct drive robot. Before the estimation, the input-output data for identification are generated in a sequential and term-by-term manner first. Then a two-layer neural network for parameter identification is proposed, in which the back-propagation training method is used to adjust the weights between neurons. The goal is to find the weights that minimize the root-mean-square error between the identification data and output of the network. With the estimated dynamics, existing trajectory-tracking algorithms, such as the well-known computed-torque method, can then be applied to make the robot move along a desired trajectory.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Yunlin Institute of Technology, Touliu, 640, Yunlin, Taiwan, R.O.C.

    Chia-Ju Wu & Ching-Huo Huang

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  1. Chia-Ju Wu
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  2. Ching-Huo Huang
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Wu, CJ., Huang, CH. Back-propagation neural networks for identification and control of a direct drive robot. J Intell Robot Syst 16, 45–64 (1996). https://doi.org/10.1007/BF00309655

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  • DOI: https://doi.org/10.1007/BF00309655

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