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.