Dynamic Event-Triggered Resilient Heading Control for Unmanned Surface Vehicle With Encrypted Data

This paper studies data-driven dynamic event-triggered heading control issues for an unmanned surface vehicle (USV) with enciphered data and aperiodic denial of service (DoS) attacks. First, we establish a compact form dynamic linearization model for the USV by employing the control input and a formulated virtual output. Next, we design a quantization-based encoding-decoding scheme, in which the data is quantized and encoded before transmission and decoded upon reception by the controller. This approach not only compresses data bits but also prevents information leakage. Meanwhile, we design a dynamic variate and develop a dynamic event-triggered mechanism to reduce the communication burden. Moreover, we employ a radial basis function neural network (RBFNN) to create an RBFNN-based compensation method to offset the effects of aperiodic DoS attacks. As a result, we propose a data-driven dynamic event-triggered resilient heading control (DET-RHC) approach, and we rigorously prove its convergence. Additionally, we verify the effectiveness of the proposed DET-RHC method through several simulation studies.