DEVELOPMENT AND TESTING OF INTELLIGENT CONTROL SYSTEMS TO IMPROVE THE PRODUCTIVITY OF TECHNOLOGICAL PROCESSES

Zhuang Xintong, Sun Hui

This article is devoted to the development and testing of intelligent control systems aimed at improving the productivity of technological processes. The relevance of the research is due to the rapid development of information technologies and the need for their introduction into the industrial sphere to optimize production cycles. The paper presents an analysis of existing approaches to the construction of intelligent control systems, including the use of artificial neural networks, fuzzy logic and genetic algorithms. Special attention is paid to hybrid methods combining the advantages of various paradigms of artificial intelligence. Research materials and methods include mathematical modeling of technological processes, development of architecture of intelligent control systems, software implementation of algorithms in high-level languages, as well as conducting experimental studies on real production data. Automated polymer product production lines and numerically controlled metalworking complexes were used as test benches. The results of the study demonstrate a significant increase in key performance indicators of technological processes when implementing the developed intelligent control systems. In particular, it was possible to achieve an increase in productivity by 12-17%, a reduction in specific energy consumption by 8-12% and a reduction in the percentage of defects by 5-9%. The proposed approaches to the construction of intelligent control systems can be adapted for a wide range of industrial enterprises in various industries, which will significantly increase the competitiveness of domestic products on the world market.

Keywords:intelligent control systems, technological processes, artificial intelligence, neural networks, fuzzy logic, genetic algorithms, production automation, optimization, energy efficiency, product quality