Intelligent Digital Solutions in Industrial Automation: From Predictive Control to Cyber-Physical Monitoring Systems

Abstract

This paper examines modern approaches to implementing intelligent digital solutions in industrial automation systems within the Industry 4.0 paradigm. The architecture of distributed cyber-physical systems combining edge computing and cloud analytics is analyzed in detail, highlighting the trade-offs between latency and computational depth. Special attention is paid to the application of machine learning methods, neural network modeling, and optimal control algorithms for solving applied tasks: predictive maintenance of equipment, energy consumption forecasting, material flow management, and monitoring of industrial and environmental safety. It is shown that the synergy of computer vision algorithms, explainable AI, and hybrid neural network models can significantly enhance the efficiency, transparency, and environmental safety of industrial complexes. The transition from deterministic control to adaptive, self-learning systems is demonstrated to be a critical factor in modernizing critical infrastructure.