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

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.