Adaptive Learning from Quantized Signals for AUV Formation Tracking Control

This paper investigates the formation tracking problem for a group of autonomous underwater vehicles (AUVs) operating under quantized communication and actuation. A novel adaptive learning framework is proposed, capable of extracting cooperative control policies directly from quantized relative measurements and quantized input signals. Unlike conventional approaches that rely on continuous signal assumptions, the developed method enables each AUV to learn and adapt its behavior in real time from coarsely quantized data, thereby enhancing robustness in digital and bandwidth-limited environments. Within a backstepping control structure, an improved quantized consensus mechanism and a hysteresis quantizer compensation strategy are integrated to mitigate quantization effects. Using Lyapunov stability theory, it is proven that all closed-loop signals remain bounded and the formation tracking errors converge to an adjustable neighborhood of zero. Simulation results demonstrate that the proposed learning-based controller achieves accurate formation tracking and exhibits strong adaptability under dual quantization constraints.