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Weighted multiple model adaptive boundary control for a flexible manipulator.

Weicun Zhang1, Qing Li1, Yuzhen Zhang1

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This study introduces a new adaptive control method for flexible manipulators facing significant uncertainties. The proposed weighted multiple model approach ensures precise movement and vibration suppression, enhancing robotic system stability.

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Flexible manipulatorboundary controlvibration controlvirtual equivalent systemweighted multiple model adaptive control

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Area of Science:

  • Robotics and Control Systems
  • Mechanical Engineering
  • Applied Mathematics

Background:

  • Flexible manipulators are prone to vibrations and parameter uncertainties, complicating precise control.
  • Existing control methods struggle with large, unknown parameter variations in these systems.

Purpose of the Study:

  • To develop a robust adaptive boundary control scheme for flexible manipulators with significant unknown parameter uncertainties.
  • To enhance the precision and stability of flexible manipulator operations under dynamic conditions.

Main Methods:

  • A weighted multiple model adaptive control strategy is proposed.
  • The manipulator's dynamics are modeled using Euler-Bernoulli beam theory and Hamilton's principle, resulting in partial differential equations.
  • Local boundary controllers are designed based on Lyapunov stability theory for each model.
  • An improved weighting algorithm and virtual equivalent system theory ensure overall system stability.

Main Results:

  • The proposed control scheme effectively manages unknown large parameter uncertainties in flexible manipulators.
  • Numerical simulations demonstrate the feasibility and effectiveness of the weighted multiple model adaptive control strategy.
  • The control strategy successfully suppresses manipulator vibrations and ensures precise movement.

Conclusions:

  • The developed weighted multiple model adaptive boundary control scheme provides a robust solution for controlling flexible manipulators with unknown uncertainties.
  • The approach enhances system stability and performance, applicable to various robotic applications.
  • This method offers a significant advancement in controlling complex, uncertain mechanical systems.