CURRENT ISSUES IN AUTOMATIC CONTROLLING OF TECHNOLOGICAL PROCESSES

This paper explores the current challenges and advancements in the automatic control of technological processes. It reviews various control strategies, including PID controllers, Model Predictive Control (MPC), fuzzy logic, and AI-based systems, highlighting their effectiveness in managing complex, nonlinear industrial processes. A primary challenge is ensuring the accuracy and reliability of sensor data, as inaccuracies can lead to poor control performance. Recent developments in sensor technology and the integration of Internet of Things (IoT) with SCADA systems are improving real-time monitoring and decision-making. The paper also addresses the complexity of industrial processes, particularly in chemical and power generation sectors, where nonlinearities and disturbances complicate control. Adaptive and robust control strategies, as well as AI and machine learning, offer potential solutions for maintaining performance in dynamic conditions. Despite progress, challenges in sensor reliability, system integration, and real-time data processing remain. The study emphasizes the need for further research to enhance the efficiency and resilience of control systems.