RSM And ANFIS Based Parameters Prediction of Robot Using GRA

  • Abstract
  • Keywords
  • References
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  • Abstract

    Ability of robot arm manipulation must be highly accurate and repeatable one. Performance uncertainty is causes by some noise factor. The effects of these factors were model to reduce the uncertainty of the robotic arm performance. In this paper highlights the prediction of output parameters robot cell data like X, Y and Z axis through Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) for reduce the performance variation of the robot. The input kinematic parameters like θ1, θ2, θ3, θ4, θ5 has been considered and the output multi objective parameters X, Y and Z axis has been converted in to single objective parameter. The graph which plots between parameters and the output response indicates the influence of the every single parameter for the performance output contribution. From the simulated values of Response Surface Methodology and Adaptive Neuro Fuzzy Inference System, the percentage of error obtained in Adaptive Neuro Fuzzy Inference System has minimum one when compared with Response Surface Methodology of prediction.



  • Keywords

    Robot, response surface methodology, adaptive neuro fuzzy inference system.

  • References

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Article ID: 24208
DOI: 10.14419/ijet.v7i4.36.24208

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