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Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer.

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Summary

This study introduces a novel control method for autonomous underwater vehicles (AUVs) that enhances trajectory tracking accuracy in challenging underwater environments. The advanced controller effectively compensates for disturbances, ensuring robust navigation.

Keywords:
autonomous underwater vehiclefull-form dynamic linearizationiterative extended state observermodel-free adaptive controlpseudo gradienttrajectory tracking

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Ocean Engineering

Background:

  • Autonomous Underwater Vehicles (AUVs) require precise trajectory tracking for various applications.
  • Underwater environments present significant challenges due to unpredictable disturbances.
  • Existing control methods often struggle with unknown environmental factors and model uncertainties.

Purpose of the Study:

  • To develop a robust and precise trajectory tracking control algorithm for AUVs.
  • To address the challenges posed by unknown underwater environmental disturbances.
  • To improve the navigation accuracy and stability of AUVs.

Main Methods:

  • Design of a model-free adaptive control (MFAC) system utilizing data-driven principles.
  • Implementation of a full-form dynamic linearization (FFDL) method for online estimation of pseudo-gradients.
  • Integration of an iterative extended state observer (IESO) with FFDL-MFAC to compensate for model errors and external disturbances.
  • Decoupling of 3D motion into horizontal and vertical components with a multi-closed-loop control structure.

Main Results:

  • The FFDL method demonstrated superiority in simulation scenarios with and without external disturbances.
  • The proposed control algorithm showed effectiveness and robustness when tested with T-SEA I AUV parameters under simulated ocean conditions (waves 0.5 m, current 0.2 m/s).
  • The multi-closed-loop structure achieved faster convergence and reduced sensitivity to parameter jumps compared to single closed-loop systems.

Conclusions:

  • The developed FFDL-MFAC with IESO provides an effective solution for precise AUV trajectory tracking.
  • The controller's ability to learn from iterations and compensate for unknown disturbances ensures reliable performance in dynamic underwater environments.
  • The study confirms the algorithm's practical applicability and robustness for real-world AUV operations.