SMC-based model-free tracking control of unknown autonomous surface vehicles

Area of Science:

• Robotics and Control Systems
• Autonomous Marine Systems
• Adaptive Control Theory

Background:

• Autonomous Surface Vehicles (ASVs) face challenges in robust tracking control due to unknown disturbances and system uncertainties.
• Existing model-based control methods struggle with complex, unpredictable marine environments.
• Need for adaptive and robust control strategies that do not rely on precise system models.

Purpose of the Study:

• To develop a robust, model-free tracking control approach for disturbed unknown ASVs.
• To enhance ASV adaptability and robustness against unknown couplings, uncertainties, and disturbances.
• To theoretically guarantee asymptotic tracking performance and strong robustness.

Main Methods:

• Innovative combination of sliding-mode control and data-driven backstepping techniques.
• Design of a data-driven backstepping sliding-mode surface for a robust model-free adaptive controller.
• Development of a data-driven adaptive law using disturbance observer and feedforward control for estimating and compensating unknowns.

Main Results:

• The devised sliding-mode-control-based model-free tracking control (SMTC) approach demonstrates strong adaptability and robustness.
• Theoretical analysis confirms asymptotic tracking performance and guaranteed robustness.
• Simulation studies validate the SMTC approach's superiority in disturbance attenuation, nonlinearity adaptation, and accurate tracking.

Conclusions:

• The proposed SMTC approach effectively addresses the robust tracking control problem for unknown and disturbed ASVs.
• The data-driven adaptive control strategies provide significant improvements in handling uncertainties and disturbances.
• The method offers a promising solution for reliable autonomous navigation in complex marine environments.