Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Abstract

This paper addresses the tracking control problem of 3D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. More specifically, a robust Nonlinear Model Predictive Control (NMPC) scheme is presented for the case of underactuated Autonomous Underwater Vehicles (AUVs) (i.e., unicycle-like vehicles actuated only in surge, heave and yaw). The purpose of the controller is to steer the unicycle-like AUV to a desired trajectory with guaranteed input and state constraints (e.g., obstacles, predefined vehicle velocity bounds, thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle's on-board sensors. In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed, by on-line generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. Finally, realistic simulation studies verify the performance and efficiency of the proposed framework.

Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV)

“…In model predictive control [75][76][77], the dynamics are used to predict system behavior, which is compared to the behavior of a desired (simpler) dynamic model, and the difference between the two is used to formulate a control that seeks to drive the actual dynamic behavior to mimic the behavior of the desired dynamic. Reference [76] also highlights a very realistic issue: actuator saturation and under actuation (driven by hardware selection). This motivates the final sections of this manuscript: an implementable operational procedure that starts with a selection of actuators, and uses their limits to determine the desired maneuver times for the autonomous trajectory generation scheme, which feeds the D.A.I.…”

“…References [67][68][69][70][71][72][73] utilize data-derived models in the various adaptive schemes, while [74] substantiates the first evolution from simple adaptive systems with the innovation of deterministic self-awareness applied to the forced van der Pol equation. References [75][76][77][78] illustrate the utilization of the system models towards optimization and prediction. Lastly, [79] is the first book devoted to the application of the methods developed here applied to space systems, while this manuscript applies the methods to underwater vehicles.…”

Trajectory Tracking of a Novel Underactuated AUV via Nonsingular Integral Terminal Sliding Mode Control

“…The control methods of underwater vehicles also include PID control and adaptive fuzzy control [14], [15], with new methods constantly being proposed. Additionally, some nonideal situations during trajectory tracking, such as obstacle avoidance or controller thrust saturation, have also been extensively studied in recent years [16]- [18].…”

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

“…During the last several decades, various control methods have been widely researched for trajectory tracking control problem of AUVs, such as the proportional-derivative (PD) control [ 5 ], the proportional integral derivative (PID) control [ 6 ], backstepping control (BSC) [ 7 ], sliding mode control (SMC) [ 8 ], fuzzy logic control (FLC) [ 9 ], neural-network-based control (NNC) [ 10 ], predictive control [ 11 ], adaptive control [ 12 ] and active disturbance rejection control (ADRC) [ 13 ]. Among them, the PD control and the PID control are the most used methods in practice due to their design simplicity and fine performance.…”

“…Nevertheless, NNC usually requires high computational costs and cannot guarantee its stability in some situations. In [ 11 ], a robust nonlinear model predictive control (NMPC) scheme is presented for underactuated AUVs considering model dynamic uncertainties and the presence of external disturbances. However, solving the optimal problem also requires high computing power.…”