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A robust neural controller for underwater robot manipulators.

M Lee1, H S Choi

  • 1Sensor Technology Research Center and School of Electrical and Electronic Engineering, Kyungpook National University, Taegu, Korea. mholee@knu.ac.kr

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
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This study introduces a robust control scheme using a neural network to enhance robot manipulator control in uncertain underwater environments. The advanced system effectively manages unpredictable forces like drag and buoyancy for improved performance.

Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Underwater robot manipulators face significant control challenges due to environmental uncertainties.
  • Conventional control methods struggle with unpredictable parameters like buoyancy, drag, and wave effects.
  • Robust control is essential for reliable operation in dynamic subsea environments.

Purpose of the Study:

  • To develop a robust control scheme for underwater robot manipulators.
  • To enhance control performance by addressing uncertainties in system parameters.
  • To improve the reliability and effectiveness of robotic operations in challenging marine conditions.

Main Methods:

  • Utilized a multilayer neural network trained with an error backpropagation learning algorithm.

Related Experiment Videos

  • Integrated the neural network as a compensator for a conventional sliding mode controller.
  • Applied the proposed control scheme to a simulated underwater robot manipulator model.

    • Main Results:

    • The proposed neural network-based controller demonstrated robust performance in simulations.
    • The controller effectively compensated for uncertainties such as buoyancy, drag, wave effects, currents, and added mass.
    • Significant improvements in control performance were observed under invalid uncertainty bound assumptions.

    Conclusions:

    • The developed robust control scheme offers an effective solution for controlling robot manipulators in uncertain underwater environments.
    • Neural network compensation enhances the adaptability and resilience of sliding mode controllers.
    • The findings support the application of advanced AI control strategies for subsea robotics.