Efficient Control of Mechatronic Systems Enabled by Generative AI for Single-Chip Microcomputers

Abstract

In recent years, research on industrial innovation and development has primarily focused on industrial automation and intelligent manufacturing. Within the field of integrating mechatronics and intelligent control, analyzing the efficient control of mechatronic systems enabled by generative AI for single-chip microcomputers can further highlight the value and significance of promoting AI technology applications. This paper examines the technical characteristics of generative AI in data generation, multimodal fusion, and dynamic adaptation, proposing lightweight model deployment strategies that compress large generative models to a range compatible with single-chip microcomputers, ensuring local real-time inference capabilities. It constructs an edge intelligent control architecture, enabling generative AI to directly participate in decision-making instruction generation, forming a new working system of perception, decision-making, and execution. Additionally, it designs a collaborative optimization training mechanism that leverages federated learning to overcome single-machine data limitations and enhance model generalization performance. At the application level, an intelligent fault prediction system is developed for early identification of equipment anomalies, an adaptive parameter optimization module is constructed for dynamically adjusting control strategies, and a multi-device collaborative scheduling engine is established to optimize production processes, providing technical support for embedded intelligent control in Industry 4.0 scenarios.