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Event-triggered adaptive tracking control for USV based on enhanced optimized backstepping technique.

Hugan Zhang1, Xianku Zhang2, Yongjin Liu2

  • 1Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.

ISA Transactions
|December 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive control method for unmanned surface vehicles, reducing communication load and improving disturbance rejection. The novel approach enhances trajectory tracking performance and system robustness against environmental factors.

Keywords:
Enhanced optimized backsteppingEvent-triggered mechanismFixed-time disturbance observerUSV trajectory tracking

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Area of Science:

  • Robotics and Control Systems
  • Marine Engineering
  • Artificial Intelligence

Background:

  • Unmanned Surface Vehicles (USVs) face challenges in trajectory tracking due to environmental disturbances, communication constraints, and complex control design.
  • Existing control methods often struggle with real-time adaptation to dynamic ocean conditions and efficient data handling.

Purpose of the Study:

  • To develop an enhanced adaptive optimal backstepping control method for USVs.
  • To address limitations in disturbance rejection, sensor communication burden, and controller complexity.

Main Methods:

  • An actor-critic reinforcement learning framework was employed for optimal control decision-making and performance evaluation.
  • Neural networks were utilized to estimate cost function gradients, simplifying calculations.
  • An event-triggered mechanism was implemented to reduce sensor communication frequency.
  • A disturbance observer was developed for real-time estimation and compensation of environmental disturbances.

Main Results:

  • The proposed method significantly reduces sensor communication frequency.
  • Real-time compensation of environmental disturbances enhances system robustness.
  • Theoretical analysis confirmed the stability and effectiveness of the control algorithm.
  • Simulation results demonstrated superior performance compared to conventional methods.

Conclusions:

  • The enhanced adaptive optimal backstepping control method provides a robust and efficient solution for USV trajectory tracking.
  • The integration of event-triggered mechanisms and disturbance observers offers significant advantages in practical USV applications.
  • The study validates the effectiveness of actor-critic reinforcement learning in complex marine control scenarios.