Model Predictive Control for Autonomous Underwater Vehicles

Gomes, Rui; Pereira, Fernando Lobo

doi:10.1016/j.procs.2019.02.006

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…MPC implementations on AUVs include trajectory planning (Shen et al, 2015), trajectory tracking (Shen et al, 2016, 2019; Yan et al, 2020), formation control (Gomes & Pereira, 2018, 2019), manipulation (Nikou et al, 2018), and docking (Nielsen et al, 2018). Most of these developments have been demonstrated in simulation studies, and very few cases of experimental and field demonstrations exist.…”

Section: Introductionmentioning

confidence: 99%

“…Second, uncertainty in state measurement and hydrodynamic modeling is high due to sensing constraints (low visibility, limited localization, lack of GPS) and the nature of the underwater environment (unsteady flow, currents, disturbances). Such uncertainty can be addressed with good state estimation (Steenson et al, 2014), with additional safety and robustness constraints (Nikou et al, 2021), or by extending the MPC to hybrid automata (including state machines or behavior trees) (Gomes & Pereira, 2019). To the best of our knowledge, there have been no real‐time MPC implementations for agile underactuated AUVs.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Bhat

Panteli

Stenius

et al. 2023

Journal of Field Robotics

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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 model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, 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 linear quadratic regulator 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.

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