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Trajectory Tracking Control for Subsea Mining Vehicles Based on Fuzzy PID Optimised by Genetic Algorithms.

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Deep-sea mining vehicles can now navigate slippery seabeds effectively. A novel adaptive path-tracking controller prevents slippage and ensures accurate course-keeping for enhanced operational efficiency.

Keywords:
deep sea mining vehiclefuzzy PIDgenetic algorithmheading controlpath following

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Area of Science:

  • Robotics and Autonomous Systems
  • Ocean Engineering
  • Control Theory

Background:

  • Deep-sea mining vehicles operate on soft, slippery seabed sediments, leading to path-tracking challenges.
  • Vehicle slippage and deviation from the intended course are significant operational issues.
  • Existing control strategies may not adequately adapt to the dynamic seabed environment.

Purpose of the Study:

  • To develop an adaptive path-tracking controller for deep-sea mining vehicles.
  • To address and mitigate slippage issues on soft seabed terrains.
  • To enhance the navigational accuracy and operational reliability of subsea mining equipment.

Main Methods:

  • Established kinematic and dynamic models of the mining vehicle, incorporating seabed slippage analysis.
  • Developed a controller based on the Stanley algorithm with a two-degree-of-freedom kinematic model.
  • Implemented fuzzy logic rules to adapt controller gain parameters (K) to varying seabed conditions and paths.
  • Designed and optimized a fuzzy PID controller to overcome experience-based limitations.
  • Integrated a dynamic model-based relationship between drive wheel acceleration and slip rate to prevent wheel slippage.
  • Utilized Recurdyn for mechanical modeling and MATLAB/Simulink for joint system simulation.

Main Results:

  • The proposed adaptive controller effectively manages lateral and heading deviations.
  • Fuzzy rule-based adjustments enabled the vehicle to adapt its control parameters to the seabed environment.
  • The integrated slip rate limitation successfully prevented drive wheel slippage.
  • Joint simulation analysis confirmed the controller's ability to maintain accurate path tracking.
  • The control strategy demonstrated significant improvements in navigation stability and precision.

Conclusions:

  • The developed adaptive path-tracking controller is a reliable solution for deep-sea mining vehicles.
  • The controller enhances operational safety and efficiency by mitigating slippage and improving navigation.
  • This approach offers a robust method for autonomous navigation in challenging subsea environments.