A Computational Intelligence Framework for Industry 4.0-based Intelligent Motion Control using AI-Integrated PLCs

Abstract

Industry 4.0 has revolutionized industrial automation by introducing smart, interconnected, and autonomous systems. However, traditional PLC-based motion control systems suffer from rigid programming, lack of adaptability, and the absence of predictive maintenance capabilities. This paper proposes a computational intelligence-based framework that integrates AI, IoT, and PLCs for intelligent motion control. The system leverages Neural Networks for self-learning control, Fuzzy Logic for real-time adaptive decision-making, and Machine Learning for predictive maintenance. A cloud-based MySQL database supports real-time monitoring and data-driven decision-making. Experimental validation demonstrates that the AI-enhanced PLC system achieves 30% faster response times, reduces motion errors by 40%, and improves predictive maintenance accuracy to 95%. These findings confirm that the proposed AI-based control framework significantly enhances industrial motion control, ensuring efficiency, scalability, and Industry 4.0 readiness.