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<p>In this manuscript, a PID controller based on adaptive neural networks for manipulating robots with <i>n</i> degrees of freedom is presented. The neural network is given by a two-layer perceptron that compensates for the unknown dynamics of the system. The weights of the output layer are estimated online by the proposed adaptation law, while the weights and thresholds in the hidden layer are random constants. A theoretical contribution of this paper is the rigorous analysis of the introduced control scheme. In addition, to demonstrate the effectiveness of the proposed controller, real-time experiments were carried out in a pendulum and a two degrees of freedom manipulator, where the proposed controller outperforms other PID and neural network controllers previously reported in the literature.</p>

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Solving trajectory tracking of robot manipulators via PID control with neural network compensation

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Marco Moran-Armenta,
  • Carlos Aguilar-Avelar,
  • Isaac Gandarilla,
  • Marlen Meza-Sánchez,
  • Javier Moreno-Valenzuela

摘要

In this manuscript, a PID controller based on adaptive neural networks for manipulating robots with n degrees of freedom is presented. The neural network is given by a two-layer perceptron that compensates for the unknown dynamics of the system. The weights of the output layer are estimated online by the proposed adaptation law, while the weights and thresholds in the hidden layer are random constants. A theoretical contribution of this paper is the rigorous analysis of the introduced control scheme. In addition, to demonstrate the effectiveness of the proposed controller, real-time experiments were carried out in a pendulum and a two degrees of freedom manipulator, where the proposed controller outperforms other PID and neural network controllers previously reported in the literature.