Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles

TL;DR

This paper introduces a robust nonlinear model predictive control scheme for underactuated underwater vehicles, enabling accurate trajectory tracking and obstacle avoidance in dynamic, uncertain environments with external disturbances.

Contribution

It presents a novel NMPC-based control method that guarantees obstacle avoidance and trajectory tracking for underactuated AUVs in real-time, despite model uncertainties and disturbances.

Findings

Successful simulation verifies control performance and robustness.

Guarantees obstacle avoidance with online updated workspace knowledge.

Maintains bounded trajectories despite external disturbances.

Abstract

Motion control of underwater robotic vehicles is a demanding task with great challenges imposed by external disturbances, model uncertainties and constraints of the operating workspace. Thus, robust motion control is still an open issue for the underwater robotics community. In that sense, this paper addresses the tracking control problem or 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. In particular, a robust Nonlinear Model Predictive Control (NMPC) scheme is presented for the case of underactuated Autonomous Underwater Vehicles (AUVs) (i.e., vehicles actuated only in surge, heave and yaw). The purpose of the controller is to steer the underactuated AUV to a desired trajectory with guaranteed input and state constraints within a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated on-line via the vehicle's on-board sensors. In particular, by considering a ball that covers the volume of the system, obstacle avoidance with any of the detected obstacles is guaranteed, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. The proposed feedback control law consists of two parts: an online law which is the result of a Finite Horizon Optimal Control Problem (FHOCP) solved for the nominal dynamics; and a state feedback law which is tuned off-line and guarantees that the real trajectories remain bound in a hyper-tube centered along the nominal trajectories for all times. Finally, a simulation study verifies the performance and efficiency of the proposed approach.