Resource-efficient Model-free Adaptive Sliding-mode Heading Control for Unmanned Surface Vehicle with Data Dropouts

This paper addresses the issue of heading control for an unmanned surface vehicle (USV) in the presence of un-certainties. We propose a model-free adaptive sliding-mode heading control method considering resource-efficient control and dual-channel data dropouts. First, a dynamic linearization model with a pseudo partial derivative is constructed, the value of the pseudo partial derivative is estimated, and a model-free sliding-mode heading controller is formulated for USV. Second, an event-triggered mechanism allows successful triggering only when the event trigger threshold is reached, thus significantly saving communication resources. Furthermore, a comprehensive compensatory strategy is devised to solve the data loss issues in both the forward and feedback channels. Ultimately, the convergence of heading control error of USV governed by the proposed method is rigorously proved, and the effectiveness of the proposed approach is verified through simulations.