Neural adaptive robust control of underactuated marine surface vehicles with input saturation

• A new saturated tracking controller is proposed for underactuated surface vehicles.
• A new second-order error dynamic model is developed to design the controller.
• Generalized saturation functions are used to limit amplitude of control signals.
• A neural adaptive robust controller is used to compensate model uncertainties.
• The tracking performance is improved for large initial tracking errors.

This paper proposes a saturated tracking controller for underactuated autonomous marine surface vehicles with limited torque. First, a second-order open-loop error dynamic model is developed in the actuated degrees of freedom to simplify the design procedure. Then, a saturated tracking controller is designed by utilizing generalized saturation functions to reduce the risk of actuator saturation. This, in turn, improves the transient performance of the control system. A multi-layer neural network and adaptive robust control techniques are also employed to preserve the controller robustness against unmodeled dynamics and environmental disturbances induced by waves and ocean currents. A Lyapunov stability analysis shows that all signals of the closed-loop system are bounded and tracking errors are semi-globally uniformly ultimately bounded. Finally, simulation results are provided for a hovercraft vehicle to illustrate the effectiveness of the proposed controller as a qualified candidate for real implementations in offshore applications.