Trajectory tracking control for underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation

In this paper, one concerns with the problem of trajectory tracking control for an underactuated unmanned surface vehicle subject to uncertain dynamics and input saturation. A first-order sliding surface and a second-order sliding surface are hired to design surge control law and yaw control law, respectively, which together form an underactuated trajectory tracking controller. Furthermore, the potential input saturation problem is solved through an auxiliary design system. Neural shunting model is introduced into the design of the controller to avoid the increase in calculation caused by variable derivation. The minimum learning parameter method of neural network replaces the traditional multilayer neural network to compensate uncertain dynamics and time-varying disturbances, which further reduces the computational burden of the controller. Besides, two adaptive robust terms are introduced to further enhance the robustness of the trajectory tracking system. Finally, comparative simulation experiments are carried out to verify the universality and superiority of the trajectory tracking control strategy.

Automated summary

This paper investigates the trajectory tracking control for an underactuated unmanned surface vehicle with uncertain dynamics and input saturation. By utilizing sliding surfaces and introducing neural networks, the controller effectively addresses the control issues and simulation experiments confirm the effectiveness and superiority of the proposed strategy.