LLM Adaptive PID Control for B5G Truck Platooning Systems
- PMID: 37447746
- PMCID: PMC10346546
- DOI: 10.3390/s23135899
LLM Adaptive PID Control for B5G Truck Platooning Systems
Abstract
This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments.
Keywords: 5G and B5G systems; V2V communication; adaptive PID control; coordination of vehicles; large language models; platooning.
Conflict of interest statement
The authors declare that they have no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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Grants and funding
- Center for Data Science & AI/Goethe University Frankfurt
- R\&D project PID2021-122580NB-I00, funded by MCIN/AEI/10.13039/501100011033 and ERDF./Universitat Politècnica de València
- DePP - Dezentrale Plannung von Platoons im Straßengüterverkehr mit Hilfe einer KI auf Basis einzelner LKW/Goethe University Frankfurt
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