Collaborative optimization system for in dustrial intelligent manufacturing and digital electromechanical systems based on artificial intelligence technology

A collaborative optimization system for industrial intelligent manufacturing and digital electromechanical systems based on artificial intelligence (AI) technology achieves real-time monitoring and smart management of the production process through data collection, analysis, and processing and digitizes modeling and simulation to achieve collaborative optimization and intelligent control. Analyzing the components and control modules, power modules, and system parameters under the electromechanical system, the traditional proportional–integral–derivative (PID) control and fuzzy neural network PID control methods were compared. The results showed that under constant load, the maximum errors between the tracking speed and the preset speed were 0.3 m/s and 0.2 m/s, respectively, when the PID control speed curve speed was 0–40 ms and 40–100 ms. When the speed curve of fuzzy neural network PID control under constant load was 0–40 ms and 40–100 ms, the maximum errors between the tracking speed and the preset speed were 0.2 m/s and 0.2 m/s, respectively. According to the results, the tracking control effect of fuzzy neural network PID control was superior.