Technologies of Automated Process Control and Automated Regulation
The course provides a holistic understanding of how to design control systems for technological processes and industrial cyber‑physical systems. It combines classical methods of automatic control with industrial automation architectures and modern digital technologies, guiding students from system architecture design to managing the dynamics of technological processes.
At the core of the course lies the idea that any industrial system represents a controllable physical process that requires a complete cycle: measuring parameters, processing data, making control decisions, and implementing automatic control algorithms. Special attention is given to integrating automation systems with advanced digital platforms, including the Industrial Internet of Things (IIoT), digital twins, intelligent data analysis methods, and the use of Large Language Models (LLMs) as tools for engineering analysis and design.
Students explore key aspects of automated process control — from the fundamentals of automatic control and PID regulation to modern intelligent control methods, industrial controllers (PLC, DCS), supervisory control and data acquisition systems (SCADA), and the integration of process control systems (PCS) with IIoT and digital platforms. They learn to model the dynamics of technological processes, design control loops and PCS architecture, and develop monitoring and supervisory control systems — all while taking into account real‑time requirements and system fault tolerance.
A central component of the course is a practical project in which students develop a concept for a process control system or production system. Step by step, they analyse the technological process, build a dynamics model, design an automatic control algorithm and PCS architecture, create a monitoring and supervisory control system, and propose integration solutions with IIoT and digital platforms. The project unfolds over the course of the semester and culminates in a presentation of the final solution.
An innovative feature of the course is the integration of Large Language Models (LLMs) into the learning process. LLMs are used as engineering assistants to analyse control system architecture, develop automation concepts, formulate mathematical process models, evaluate control algorithms, and prepare project documentation. Students learn not only to leverage LLM capabilities but also to critically evaluate their outputs, fostering engineering reflection and sound decision‑making skills.
Upon completing the course, students gain comprehensive knowledge of automated control system architecture, principles of control loop design, methods for modelling technological processes, and the operating principles of industrial controllers and SCADA systems. They acquire the ability to analyse process dynamics, design automatic control and monitoring systems, and integrate control solutions with digital platforms. Practical skills include systems analysis of technological processes, designing automation architecture and operator interfaces, and using LLMs effectively in engineering tasks — preparing graduates to tackle real‑world challenges in modern industrial automation and intelligent production.
OBJECTIVES
Developing a systemic understanding of automated process control principles;
Mastering methods for analysing the dynamics of technological systems;
Developing skills in designing automatic control systems;
Forming competencies in developing process control system (PCS) architecture;
Mastering modern approaches to integrating control systems with digital platforms;
Developing skills in systems design of intelligent production systems;
Fostering a culture of using LLM as a tool for engineering analysis and control system design.
KEY TASKS
Studying the architecture of automated process control systems;
Analysing dynamics and modelling technological processes;
Mastering automatic control methods;
Studying principles of control system design based on PLC and DCS;
Developing algorithms for process control;
Studying supervisory control systems (SCADA);
Mastering methods of integrating control systems with IIoT and digital platforms;
Studying intelligent data analysis methods for process control;
Developing skills in systems design of intelligent production systems;
Developing skills in using LLM for engineering analysis, design, and documentation.
Main topics of the course:
1. Industrial Automation Architecture and Cyber‑Physical Production Systems (ISA‑95 levels). Introduces the structure of modern industrial automation and the role of process control, covering ISA‑95 levels and smart factory architecture, with a focus on automated process control systems (APCS).
2. Process Dynamics and Modelling. Explores the dynamic behaviour of technological processes (such as temperature, level, and speed control), teaching students how to build and formalise mathematical models for real‑world systems.
3. Fundamentals of Automatic Control (Feedback Control Systems). Covers feedback principles and system stability, analysing control loops in technological processes and helping students understand how closed‑loop systems maintain desired performance.
4. PID Control and Controller Tuning. Focuses on PID control algorithms and their tuning methods, enabling students to calculate parameters and design basic control algorithms for model processes.
5. Advanced Process Control Strategies. Examines cascade, adaptive, and predictive control methods, comparing different control strategies and helping students select the most efficient approach for complex processes.
6. Industrial Controllers and Real‑Time Systems (PLC, DCS). Introduces industrial control architectures, including programmable logic controllers (PLC) and distributed control systems (DCS), with hands‑on experience in developing control logic and studying relevant standards (IEC 61131‑3).
7. SCADA and Supervisory Control Systems (SCADA, HMI). Covers supervisory control and data acquisition systems, focusing on human‑machine interaction (HMI) and teaching students to design operator interfaces and monitoring systems.
8. Integration of APCS with IIoT and Digital Twins. Explains how to integrate automated process control with digital technologies, including the Industrial Internet of Things (IIoT) and digital twins, and guides students in developing digital process models.
9. AI‑Driven Process Control. Introduces the use of artificial intelligence in industrial automation, reviewing modern intelligent control technologies and showing how to incorporate AI elements into control systems for improved performance.
10. Integrated Control Systems for Intelligent Production. Synthesises all course knowledge into a unified control system, focusing on autonomous manufacturing systems. Students complete their course project, prepare a final report, and present their integrated solution, demonstrating mastery of industrial automation concepts.
