Under-Actuated Motion Control of Haidou-1 ARV Using Data-Driven, Model-Free Adaptive Sliding Mode Control Method

Area of Science:

• Robotics
• Control Systems
• Ocean Engineering

Background:

• Autonomous Research Vehicles (ARVs) face complex under-actuated motion control challenges.
• Uncertainties like external disturbances and parameter variations significantly impact ARV navigation.
• Existing control methods may struggle with dynamic parameter changes and external perturbations.

Purpose of the Study:

• To develop a robust control strategy for the Haidou-1 ARV.
• To address the under-actuated motion control problem in the presence of significant uncertainties.
• To improve the maneuvering capabilities of the Haidou-1 ARV for speed, heading, and depth control.

Main Methods:

• Proposing a data-driven, model-free adaptive sliding mode control (MFASMC) approach.
• Integrating Model-Free Adaptive Control (MFAC) with Sliding Mode Control (SMC).
• Utilizing real-time measurement data without relying on mathematical modeling information.

Main Results:

• The MFASMC method demonstrated effective control of the Haidou-1 ARV's speed, heading, and depth.
• The control strategy proved robust against wide variations in dynamic parameters and external disturbances.
• Simulations confirmed superior performance compared to traditional PID and MFAC methods.

Conclusions:

• The proposed MFASMC approach offers a viable solution for under-actuated ARV motion control.
• MFASMC enhances ARV navigation accuracy and stability under uncertain conditions.
• This data-driven method provides a significant advancement in autonomous marine vehicle control.