Nonlinear Model Predictive Control for Hydrobatics: Experiments with an Underactuated AUV

Abstract

Hydrobatic Autonomous Underwater Vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle-avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems - this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of Model Predictive Control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear MPC (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in ROS, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and LQR based controllers in terms of rise-time, overshoot, steady-state error and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.

Inspection and maintenance of industrial infrastructure with autonomous underwater robots

“…Other hydrobatic robots include MARINS (Maritime Research Institute Netherlands) modular AUV (mAUV) ( Cozijn et al, 2019 ) and the SAM AUV developed by the Swedish Maritime Robotics Center (SMaRC) ( Bhat et al, 2022 ). In contrast to the Cuttlefish, both have a streamlined torpedo shape and are not intended for use in intervention tasks.…”

“…It is controlled using a state feedback based PID controller based on proprioceptive sensors ( De Kruif et al, 2019 ). The AUV SAM, on the other hand, is underactuated having only one thruster with two counter-rotating blades at the stern, and a moveable mass, rotating weights and a buoyancy trim system for roll, pitch, and depth control as stated by Bhat et al ( Bhat et al, 2022 ). The control is also based on state feedback but uses nonlinear Model Predictive Control (MPC) for optimization ( Bhat et al, 2022 ).…”

A Novel Control Architecture Based on Behavior Trees for an Omni-Directional Mobile Robot

“…In short, it is an architecture that uses modularity, hierarchies, and feedback. Exploring the potential of BTs has already proved effective [26,27]. The architecture shown in Figure 2 comprises five main modules.…”

“…However, approaches for implementing them in combination with higher-level representations to generate complex maneuvering capabilities have yet to be examined; ref. [26] was the only relevant work found in the literature in this regard. Other efforts have been made by the Nav2 (https://navigation.ros.org, accessed on 10 July 2023) developers, who used BTs to integrate navigation control logic.…”

“…Thus, it provides a powerful and flexible tool for controlling robotic systems, allowing them to handle complex scenarios and adapt to changing situations while maintaining robustness and reliability [24]. By combining controller policies with BTs, relatively simple controllers can be combined to generate complex maneuvering capabilities [26]. BTs can offer a modular and robust framework for hybrid control, enabling goal-based and deliberative task execution [27].…”