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A novel neural network-based nonlinear controller for shipboard rotary cranes against random wave interference.

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Area of Science:

  • Robotics and Control Systems
  • Ocean Engineering
  • Mechanical Engineering

Background:

  • Shipboard rotary cranes are complex, underactuated systems.
  • They are susceptible to nonlinear dynamics, ocean wave/current interference, and unknown parameters.
  • Precise load transportation is critical in marine operations.

Purpose of the Study:

  • To propose a robust control method for 3D shipboard rotary cranes.
  • To address challenges posed by nonlinearities, external interferences, and parameter uncertainties.
  • To enhance the precision and stability of load handling operations.

Main Methods:

  • Development of a neural network adaptive control scheme.
  • Implementation of an adaptive law to compensate for unknown system parameters.
  • Mathematical stability analysis using Lyapunov theorem and LaSalle's invariance principle.

Main Results:

  • The proposed control method effectively compensates for complex interferences.
  • Adaptive laws successfully mitigate the influence of unknown parameters.
  • Simulations show load swing angle peaks below 2.6 degrees.
  • Positioning accuracy error of the cantilever remained within 0.16 degrees.

Conclusions:

  • The neural network adaptive control method is effective for 3D shipboard rotary cranes.
  • The approach ensures stable and accurate load transportation under challenging marine conditions.
  • The control strategy enhances the reliability of crane operations at sea.