Data-Enabled Predictive Control and Guidance for Autonomous Underwater Vehicles

Abstract

This paper presents a fully data-driven control framework for autonomous underwater vehicles (AUVs) based on Data-Enabled Predictive Control (DeePC). The approach eliminates the need for explicit hydrodynamic modeling by exploiting measured input-output data to predict and optimize future system behavior. Classic DeePC was employed in the heading control, while a cascaded DeePC architecture is proposed for depth regulation. For 3-D waypoint path following, the Adaptive Line-of-Sight algorithm is extended to a predictive formulation and integrated with DeePC. All methods are validated in extensive simulation on the REMUS~100 AUV and compared with classical PI/PID control. The results demonstrate superior tracking performance and robustness of DeePC under ocean-current disturbances and nonlinear operating conditions, while significantly reducing modeling effort.