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Cooperative control for a ROV-based deep-sea mining vehicle with learned uncertain nonlinear dynamics.

Yuheng Chen1, Haicheng Zhang2, Weisheng Zou1

  • 1College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan 410082, China.

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|March 8, 2025
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This study introduces a novel remotely-operated vehicle (ROV)-based deep-sea mining system to prevent track slippage issues. The new system utilizes a cooperative control strategy for enhanced traction and sinking control, improving deep-sea mining efficiency.

Keywords:
Deep-sea miningDynamic modelingModel predictive controlPath trackingROV

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Area of Science:

  • Robotics
  • Ocean Engineering
  • Control Systems

Background:

  • Traditional deep-sea mining vehicles face significant challenges with track slippage, limiting operational efficiency and reliability.
  • Existing systems often struggle with precise control in complex underwater environments.

Purpose of the Study:

  • To propose an enhanced remotely-operated vehicle (ROV)-based deep-sea mining system (RDMV) to overcome track slippage limitations.
  • To develop a cooperative control strategy for improved traction and sinking control of the RDMV.
  • To investigate a learning-based model predictive control (LMPC) for robust disturbance rejection.

Main Methods:

  • Established the dynamic model of the ROV-based Deep-sea Mining Vehicle (RDMV) using Lagrangian mechanics.
  • Developed a distributed model predictive control (DMPC) for virtual speed control laws.
  • Implemented a learning-based model predictive control (LMPC) with a Kinky Inference (KI) prediction function for optimal control input and disturbance estimation.

Main Results:

  • The LMPC controller demonstrated feasibility and superiority in individual ROV motion control.
  • Numerical simulations confirmed the effectiveness of the cooperative control strategy for the RDMV.
  • The proposed system successfully addresses the track slippage bottleneck in deep-sea mining.

Conclusions:

  • The novel ROV-based deep-sea mining system offers a superior alternative to traditional tracked vehicles.
  • The cooperative control strategy and LMPC controller provide robust and efficient operation for deep-sea mining robots.
  • This advancement enhances the potential for reliable and productive deep-sea resource extraction.