Integration of Large Language Models in Control of EHD Pumps for Precise Color Synthesis
Yanhong Peng, Ceng Zhang, Chenlong Hu, Zebing Mao
TL;DR
This paper tackles precise color synthesis by connecting Large Language Models with Arduino-driven Electrohydrodynamic pumps. It proposes a framework where a fine-tuned LLM interprets natural-language color requests and emits Arduino code to control three EHD pumps for RGB mixing. Conceptual experiments suggest high potential for accurate color outputs, fast interpretation, and reliable pump operation, contingent on calibration and real-world validation. The work demonstrates a pathway for AI-enabled, intuitive control of physical systems within industrial automation, with scalability to additional modules and tasks.
Abstract
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.