Adaptive Learning from Quantized Signals for AUV Formation Tracking Control

This paper investigates the formation tracking problem for a group of autonomous underwatervehicles (AUVs) operating under quantized communication and actuation. A noveladaptive learning framework is proposed, capable of extracting cooperative control policiesdirectly from quantized relative measurements and quantized input signals. Unlike conventionalapproaches that rely on continuous signal assumptions, the developed methodenables each AUV to learn and adapt its behavior in real time from coarsely quantizeddata, thereby enhancing robustness in digital and bandwidth-limited environments. Withina backstepping control structure, an improved quantized consensus mechanism and ahysteresis quantizer compensation strategy are integrated to mitigate quantization effects.Using Lyapunov stability theory, it is proven that all closed-loop signals remain boundedand the formation tracking errors converge to an adjustable neighborhood of zero. Simulationresults demonstrate that the proposed learning-based controller achieves accurateformation tracking and exhibits strong adaptability under dual quantization constraints.