Robust Energy-Optimal Control for 3-D Path-Following of Autonomous Underwater Vehicles Under Ocean Currents

In this work, we propose a robust energy-optimal control that achieves 3-D path following for autonomous underwater vehicles (AUVs) in environments with ocean currents. The actual algorithm is decomposed into two elements: setpoint computation and setpoint tracking. For setpoint computation, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing vehicle propulsion energy considering the uncertainty set defined by the state estimate and associated uncertainty. A line-of-sight (LOS)-based guidance law, which integrates direct and indirect drift angle compensation for reduced path-following error and path-convergence time, is established to compute the yaw angle setpoints. Two setpoint-tracking model predictive controllers, minimizing a weighted sum of setpoint-tracking error and control efforts, are designed to control horizontal and vertical vehicle motion with low computational complexity. Simulation is conducted on a lawnmower-type mission under different flow conditions in the presence of measurement noises and biased ocean current estimates. The performance robustness in path following and energy saving of the proposed approach is verified through extensive numerical and theoretical analysis.

Automated summary

This work proposes a robust energy-optimal control method for achieving 3-D path following for autonomous underwater vehicles in environments with ocean currents. The method optimizes setpoints considering uncertainty and uses a line-of-sight-based guidance law for calculating yaw angle setpoints. Model predictive controllers are designed for controlling horizontal and vertical vehicle motion, with simulation confirming the performance of the proposed approach.