Data-Based Encryption Iterative Learning Heading Control for Unmanned Surface Vehicles

This paper investigates a data-driven iterative learning heading control problem for unmanned surface vehicles (USVs) with encoding-decoding mechanisms. First, a compact form dynamic linearized model of the USV is established using dynamic linearization techniques and redefined outputs. Then, an encoding-decoding scheme is designed, which encodes the data before transmission and decodes the data received by the controller. This strategy compresses the data and offers protection from potential breaches of information. Finally, the convergence of the designed method is theoretically analyzed, and simulation results demonstrate its effectiveness in enhancing heading control performance.