Reinforcement learning-based NMPC for tracking control of ASVs: Theory and experiments

We present a reinforcement learning-based (RL) model predictive control (MPC) method for trajectory tracking of surface vessels. The proposed method uses an MPC controller in order to perform both trajectory tracking and control allocation in real-time, while simultaneously learning to optimize the...

Full description

Saved in:
Bibliographic Details
Published inControl engineering practice Vol. 120; p. 105024
Main Authors Martinsen, Andreas B., Lekkas, Anastasios M., Gros, Sébastien
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2022
Subjects
Dynamic positioning
Model predictive control
Optimal control
Reinforcement learning
Surface vessels
System identification
Trajectory tracking
Model predictive control
Surface vessels
Dynamic positioning
Trajectory tracking
Optimal control
Reinforcement learning
System identification
Online AccessGet full text
ISSN0967-0661
1873-6939
DOI10.1016/j.conengprac.2021.105024

Cover

More Information
Summary:We present a reinforcement learning-based (RL) model predictive control (MPC) method for trajectory tracking of surface vessels. The proposed method uses an MPC controller in order to perform both trajectory tracking and control allocation in real-time, while simultaneously learning to optimize the closed loop performance by using RL and system identification (SYSID) in order to tune the controller parameters. The efficiency of the method is evaluated by performing simulations on the unmanned surface vehicle (USV) ReVolt, as well as simulations and sea trials on the autonomous urban passengers ferry milliAmpere. Our results demonstrate that the proposed method is able to outperform other state of the art methods both in tracking performance, as well as energy efficiency.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2021.105024