Data-Enabled Predictive Control and Guidance for Autonomous Underwater Vehicles

TL;DR

This paper introduces a data-driven control framework for autonomous underwater vehicles that uses measured data to predict and optimize behavior, eliminating the need for explicit hydrodynamic models and demonstrating superior performance in simulations.

Contribution

It develops a fully data-driven predictive control approach for AUVs, integrating DeePC with adaptive algorithms for 3D path following, reducing modeling effort and improving robustness.

Findings

DeePC outperforms classical PI/PID control in simulations.

The framework achieves superior tracking and robustness under disturbances.

Significantly reduces the need for explicit hydrodynamic modeling.

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.