Adaptive path tracking control of underactuated unmanned surface vessels based on the frontal point of view

Abstract

To solve the problems in common line-of-sight-based path-tracking controllers, including poor speed adaptability, difficult parameter tuning process, and poor anti-interference ability, this paper presents a novel adaptive path tracking control scheme of underactuated surface vessels based on the frontal point of view. First, based on the frontal point of view in traditional line-of-sight based guidance, an adaptive guidance model is introduced using the combination of the tracking deviation state and the Sigmoid function. Next, an adaptive tracking controller of the vessel speed and heading is designed based on the radial neural network and the dynamic surface control technology of minimum parameter learning. Based on the Lyapunov stability theory, the designed controller can guarantee that all tracking errors are uniform and finally bounded. The results of the outfield experiments demonstrate good performance and high robustness of the proposed control scheme under the effect of ocean disturbances.