Intelligent control technology of engineering electrical automation for PID algorithm.

The article focuses on the development of an Override-Controlled Definitive Performance Scheme (OCDPS) designed to enhance the efficiency and reliability of electrical machine operations in smart industries through automation. OCDPS integrates proportional-integral-derivative (PID) controllers with predictive learning based on previous operational logs to confine machine operations within allocated time intervals, thereby preventing loop failures, operational overrides, and downtime. Comparative analysis with existing models—Iterative Learning Control (ILC), Adaptive Swarm Learning Process (ASLP), and Twin Delayed Deep Deterministic Policy Gradient (TD3)—demonstrates that OCDPS achieves lower downtime, fewer failures, reduced override time, and higher efficiency and prediction accuracy. The scheme utilizes control value adjustments informed by operational load, time, and predictive learning to maintain consistent control loops and improve overall machine performance without increasing human intervention.