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LLM Adaptive PID Control for B5G Truck Platooning Systems.

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This study introduces an AI-driven adaptive PID controller for truck platooning in 5G networks. It uses deep learning and large language models to improve performance and safety, addressing communication challenges.

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Area of Science:

  • Intelligent Transportation Systems
  • Wireless Communication Networks
  • Artificial Intelligence in Control Systems

Background:

  • Truck platooning requires advanced control systems for safety and efficiency.
  • 5G and Beyond 5G (B5G) networks offer enhanced connectivity for vehicle-to-vehicle (V2V) communication.
  • Traditional PID controllers may struggle with dynamic network conditions like latency and packet loss.

Purpose of the Study:

  • To develop and evaluate an adaptive PID controller for truck platooning using AI.
  • To investigate the impact of communication parameters (latency, packet loss, range) on controller performance.
  • To explore the integration of Large Language Models (LLMs) for real-time system updates.

Main Methods:

  • Developed a Deep Learning (DL) model to emulate an adaptive PID controller.
  • Simulated communication impairments including latency, packet loss, and limited range.
  • Utilized GPT-3.5-turbo (a Large Language Model) for instantaneous performance feedback to the controller.

Main Results:

  • The DL-emulated adaptive PID controller demonstrated effectiveness in truck platooning scenarios.
  • Controller performance was analyzed under various communication constraints, highlighting robustness.
  • LLM integration provided real-time updates, enhancing system responsiveness.

Conclusions:

  • AI-enhanced adaptive PID controllers are viable for truck platooning in B5G networks.
  • LLMs show promise for real-time control system optimization in advanced communication environments.
  • This research provides a foundation for safer and more efficient autonomous vehicle operations.