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This study introduces a novel neural network model for robotic manipulator control, optimizing performance in systems with unknown parameters and dead zones using reinforcement learning.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Robotic manipulators often face challenges with unknown parameters and dead zones, hindering precise control.
  • Existing control methods may struggle with the complexity and adaptability required for such systems.

Purpose of the Study:

  • To develop and evaluate a novel neural network model for reinforcement learning-based control of robotic manipulators.
  • To address challenges posed by unknown system parameters and dead zones in robotic manipulation.

Main Methods:

  • A three-network architecture was proposed: a state network for prediction, an action network for policy learning, and a critic network for performance estimation.
  • The model utilizes a reinforcement learning control scheme to optimize a performance index.
  • Convergence analysis of the learning methods was conducted.

Main Results:

  • The proposed neural network model effectively controls a simulated two-link robotic manipulator.
  • The model demonstrated stability and effectiveness in handling unknown parameters and dead zones.
  • The integrated three-network approach successfully optimized the performance index.

Conclusions:

  • The developed neural network model offers a robust solution for controlling robotic manipulators with uncertainties.
  • Reinforcement learning provides a viable framework for adaptive and stable robotic control.
  • The proposed model shows significant potential for real-world robotic applications requiring precise manipulation.