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Control of a Robotic Manipulator Using Artificial Neural Networks with On-line Adaptation

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Abstract

An adaptive neural system for positioning control of a PUMA 560 manipulator is presented". The computed torque method was implemented with a Multi-Layer Perceptron with on-line learning. The control scheme is implemented into two phases. The first one is the off-line phase in which the neural network is trained with previously known control actions. The second one is the on-line phase in which the neural network parameters are adapted while controlling the manipulator. The control system is able to respond to changes in the manipulator model and to load disturbances. As will be shown, control system performance is improved with the on-line learning strategy presented in this paper.

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Authors and Affiliations

  1. Instituto Católico de Minas Gerais, Coronel Fabriciano, MG, Brazil

    Roselito A. Teixeira

  2. Depto. Engenharia Eletrónica, UFMG, Belo Horizonte, Brazil

    Antônio de P. Braga

  3. Depto. Engenharia Eletrónica, UFMG, Belo Horizonte, Brazil

    Benjamim R. de Menezes

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  1. Roselito A. Teixeira
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  2. Antônio de P. Braga
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  3. Benjamim R. de Menezes
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Teixeira, R.A., Braga, A.d.P. & Menezes, B.R.d. Control of a Robotic Manipulator Using Artificial Neural Networks with On-line Adaptation. Neural Processing Letters 12, 19–31 (2000). https://doi.org/10.1023/A:1009694129740

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  • DOI: https://doi.org/10.1023/A:1009694129740

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