Input-Quantized Output Feedback Control for Autonomous Underwater Vehicles with Tunnel Prescribed Performance

This paper investigates a tunnel performance guaranteed trajectory tracking problem for an autonomous underwater vehicle (AUV) with lumped disturbances and quantized inputs. To estimate lumped disturbances, a quantization-based neural predictor (QNP) is constructed. With the error transformation methodology, a tunnel guidance law is devised to stabilize the transient and steady behavior of track error. By using the estimated information, a QNP-based control law is derived to track the guidance signals for AUV. The developed control scheme ensures that the total closed-loop system is input-to-state stable (ISS). Simulation results demonstrate the effectiveness of the proposed control method.