  This paper presents an innovative approach to integrating Large Language
Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for
precise color synthesis in automation systems. We propose a novel framework
that employs fine-tuned LLMs to interpret natural language commands and convert
them into specific operational instructions for EHD pump control. This approach
aims to enhance user interaction with complex hardware systems, making it more
intuitive and efficient. The methodology involves four key steps: fine-tuning
the language model with a dataset of color specifications and corresponding
Arduino code, developing a natural language processing interface, translating
user inputs into executable Arduino code, and controlling EHD pumps for
accurate color mixing. Conceptual experiment results, based on theoretical
assumptions, indicate a high potential for accurate color synthesis, efficient
language model interpretation, and reliable EHD pump operation. This research
extends the application of LLMs beyond text-based tasks, demonstrating their
potential in industrial automation and control systems. While highlighting the
limitations and the need for real-world testing, this study opens new avenues
for AI applications in physical system control and sets a foundation for future
advancements in AI-driven automation technologies.
