Design and Implementation of an Adaptive Control System Based on Deep Learning

Abstract

Precision and real-time performance in industrial temperature control have always been significant challenges in manufacturing. To address this issue, we developed an adaptive control system based on deep learning. This system employs an improved reinforcement learning algorithm, combined with parallel computing and instruction set optimization, achieving a millisecond-level control cycle. Experimental results show that the system maintains temperature control errors within ±1°C and exhibits excellent anti-interference capabilities. It demonstrates rapid response and adaptability when faced with sudden anomalies. This innovation provides a new technological solution for enhancing industrial production efficiency and product quality, advancing intelligent manufacturing.