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Related Experiment Videos

LLM-assisted adaptive dynamic programming and prescribed performance control for USV trajectory tracking under

Siyuan Wang1, Yuanqiao Wen2, Qi Zhang2

  • 1State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, 430063, China; National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, 430063, China; School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, China.

ISA Transactions
|July 10, 2026
PubMed
Summary

This study introduces a novel control strategy for Unmanned Surface Vehicles (USVs) using Prescribed Performance Control (PPC) and Adaptive Dynamic Programming (ADP), enhanced by a Large Language Model (LLM) for efficient parameter tuning.

Keywords:
Adaptive dynamic programmingDeep neural networkLarge language modelPrescribed performance controlUnmanned surface vehicle

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Unmanned Surface Vehicles (USVs) face challenges in complex maritime environments due to disturbances and control parameter tuning difficulties.
  • Existing control methods often struggle with multi-parameter controller optimization and adapting to uncertainties.

Purpose of the Study:

  • To develop an advanced control strategy for USVs that improves trajectory tracking accuracy and simplifies parameter tuning.
  • To integrate Prescribed Performance Control (PPC), Adaptive Dynamic Programming (ADP), and Large Language Models (LLMs) for robust USV control.

Main Methods:

  • A backstepping control strategy combining PPC and a hierarchical ADP architecture using Deep Neural Networks (DNNs) to handle uncertainties.
  • Implementation of a Large Language Model (LLM) for intelligent, multimodal-assisted parameter tuning to overcome manual tuning inefficiencies.
  • Lyapunov stability theory was used to prove the Semi-Global Uniform Ultimate Boundedness (SGUUB) of tracking errors.

Main Results:

  • The proposed integrated control method significantly enhances trajectory tracking accuracy compared to traditional approaches.
  • The LLM-assisted tuning mechanism demonstrated improved efficiency and provided valuable insights for parameter optimization.
  • The system effectively approximated unknown disturbances and model uncertainties, and solved the stochastic Hamilton-Jacobi-Bellman (HJB) equation.

Conclusions:

  • The combined PPC-ADP strategy with LLM-assisted tuning offers a superior solution for USV control in challenging environments.
  • This approach enhances tracking performance and drastically reduces the complexity and time required for control parameter optimization.
  • The study presents a novel perspective on leveraging AI, specifically LLMs, for advanced control system parameter tuning.