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This study introduces a model-free machine learning framework for robotic manipulator control, enabling systems to follow desired trajectories using reservoir computing and partially observed states, even with noise and uncertainties.

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Area of Science:

  • Robotics and Control Engineering
  • Machine Learning Applications
  • Dynamical Systems Theory

Background:

  • Nonlinear tracking control is crucial for robotics, but traditional methods demand complete system model knowledge.
  • Designing controllers often requires full state information, which is not always available in real-world applications.

Purpose of the Study:

  • To develop a model-free machine learning framework for controlling robotic manipulators with partially observed states.
  • To implement a novel control strategy using reservoir computing for enhanced tracking capabilities.

Main Methods:

  • A model-free, machine learning framework was developed for a two-arm robotic manipulator.
  • Reservoir computing was employed as the controller, utilizing partially observed states.
  • Stochastic input was used for training, with observed states and their immediate future as input components.

Main Results:

  • The framework demonstrated effective control for tracking various periodic and chaotic signals.
  • The control system exhibited robustness against measurement noise, disturbances, and system uncertainties.
  • The model-free approach successfully controlled the robotic manipulator using only partial state observations.

Conclusions:

  • The developed model-free, machine learning framework offers a viable alternative to traditional model-based control for robotic manipulators.
  • Reservoir computing provides an effective method for implementing tracking control with limited state information.
  • This approach enhances the applicability of robotic systems in complex and uncertain environments.