Quantized‐Based Data‐Driven Iterative Learning Heading Control for Unmanned Surface Vehicles With Data Dropouts

ABSTRACT This paper studies a model‐free adaptive iterative learning heading control problem for unmanned surface vehicles (USVs) with quantized data and data dropouts. First, a compact form dynamic linearization model is established for USVs using a dynamic linearization technique and a redefined scheme. To address data dropout issues, a comprehensive compensation strategy is developed. In addition, a logarithmic quantization mechanism is introduced to reduce the transmission burden. Based on these elements, a quantized model‐free adaptive iterative learning control approach is designed. The convergence of the heading control error of USVs governed by the proposed method is rigorously proven, and its effectiveness is verified through simulation results.