Novel adaptive law for super-twisting controller: USV tracking control under disturbances

Area of Science:

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

Background:

• Unmanned Surface Vehicles (USVs) require robust tracking control for effective operation.
• Existing adaptive control strategies often involve complex parameter tuning and can suffer from performance limitations.
• Ensuring stability and robustness against uncertainties is critical for USV navigation.

Purpose of the Study:

• To introduce a novel adaptive super-twisting control algorithm for USV tracking control.
• To enhance controller performance through simplified parameter adjustment and smoother dynamics.
• To guarantee robustness against unknown disturbances, mitigate chattering, and ensure finite-time convergence.

Main Methods:

• Development of an adaptive law using a Lyapunov stability approach.
• Formulation of conditions for robustness, chattering mitigation, and finite-time convergence.
• Design and implementation of a trajectory tracking controller for a USV prototype.

Main Results:

• The proposed adaptive control strategy demonstrated effective trajectory tracking under payload variations and environmental perturbations.
• Numerical simulations and experimental results validated the controller's performance and robustness.
• The adaptive law, requiring fewer parameter adjustments, improved controller dynamics and performance compared to other methods.

Conclusions:

• The novel adaptive super-twisting control algorithm offers a robust and efficient solution for USV tracking.
• The simplified control parameter adjustment and smooth dynamics present significant advantages over existing adaptive strategies.
• The method's effectiveness is confirmed by experimental validation on a vessel prototype, highlighting its practical applicability.