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July 26, 2026Ships and Offshore Structures

Tube-based deep Koopman model predictive control for planar trajectory tracking of autonomous underwater vehicles

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Authors

YXYeyang XuCZChaoliang ZhongQLQiang Lv

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Overview

Randomized trial demonstrates improved AUV trajectory tracking under disturbances, indicating a novel control methodology.

Key Points

  • The aim is to enhance trajectory tracking for autonomous underwater vehicles (AUVs) using a tube-based deep Koopman model predictive control framework.
  • Developed a tube-based MPC framework with nominal and ancillary components.
  • Utilized deep neural networks to learn lifting functions and construct a finite-dimensional linear Koopman model.
  • Established input-to-state stability (ISS) for the closed-loop system.
  • Numerical simulations show accurate trajectory tracking despite external disturbances and model uncertainties.
  • Comparative analysis indicates significant online computational burden reduction compared to traditional nonlinear model predictive control (NMPC).

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a65a890d3aea3239cd78ecfhttps://doi.org/10.1080/17445302.2026.2703694
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