Publication in the Diário da República: Despacho n.º 8500/2020 - 03/09/2020
6 ECTS; 1º Ano, 1º Semestre, 28,0 T + 28,0 PL + 5,0 OT + 2,0 O , Cód. 37783.
Lecturer
- Manuel Fernando Martins de Barros (1)(2)
(1) Lead Professor
(2) Teaching Professor
Prerequisites
Not applicable.
Objectives
Recent trends in globalization, mobile devices, remote operations, and systems integration are changing how distributed control systems (DCS) and supervisory control and data acquisition (SCADA) are implemented. This discipline is designed with these trends in mind, while also addressing and studying the main components of a DCS. The focus is placed on DCS operation, industrial communication networks and protocols, HMI interface design, and alarms. Topics of importance to engineers and field operators are covered, such as the study of some of the latest generation of advanced process controllers, the study of robust, real-time data communication networks for communication with industrial devices, the study of high-level communications based on the IIoT concept and process interoperability, and the manipulation of databases and cloud-based web tools.
By the end of this course unit, students should be able to:
OA1 - Analyze Distributed Control Systems (DCS/SDC) and SCADA system architectures, evaluating their functional characteristics, performance requirements, interoperability, and suitability for different industrial contexts within the scope of Industry 4.0 and IIoT.
OA2 - Analyze and evaluate different industrial communication technologies, including CAN, Modbus, Industrial Ethernet, MQTT, and industrial wireless networks (IEEE 802.15.4, ZigBee, WirelessHART, and ISA100), justifying their selection based on reliability, time determinism, security, and scalability requirements.
OA3 - Create and validate real-time distributed applications based on embedded systems, using RTOS and/or ROS2, evaluating the impact of scheduling, synchronization, and communication strategies on the overall system performance.
OA4 - Critically evaluate advanced industrial control solutions, including approaches based on artificial intelligence and fuzzy logic, comparing their performance with conventional control strategies in different application scenarios.
OA5 - Create digital integration solutions for industrial environments, using IIoT technologies, databases, web applications, cloud platforms, Node-RED and MQTT, ensuring interoperability between operational and supervisory levels.
OA6 - Design, develop and evaluate a distributed industrial supervision and control application that integrates data acquisition, industrial communications, information storage, graphical visualization and SCADA capabilities, demonstrating the ability to solve complex problems in a realistic context.
OA7 - Evaluate the technical, economic and operational trade-offs associated with the development of distributed control systems, proposing innovative and sustainable solutions to emerging challenges of industrial digitalization.
Program
1. Introduction to Distributed Control Systems
- Fundamental concepts of distributed control systems.
- DCS and SCADA architectures.
- Trends in Industry 4.0 and IIoT.
- Presentation of case studies and projects.
2. Embedded Systems Architecture
- Hardware platforms for distributed control.
- Microcontrollers and microprocessors.
- Input/output structures, memory, and interrupts.
- Development of embedded applications.
- Multitasking executives and distributed embedded systems.
3. Real-Time Systems
- Concepts and classification of real-time systems.
- Scheduling and scalability analysis.
- Task management, semaphores, and message queues.
- Study of FreeRTOS.
- Event-Triggered and Time-Triggered communication.
4. Distributed Control Systems (DCS) and SCADA Systems
- Components of industrial control systems;
- Evolution of DCS systems.
- Modern architectures of industrial supervision.
- Human-Machine Interfaces (HMI).
- Supervision, alarm and event management.
- SCADA software.
- Integration between field, control and supervision systems.
5. Artificial Intelligence, Intelligent Control and Autonomous Systems in Industry 4.0
- Evolution of advanced control systems;
- Fundamentals of Artificial Intelligence applied to industrial automation;
- Fuzzy inference systems and fuzzy logic controllers;
- Comparison between PID control and fuzzy control;
- AI applications in supervision, diagnosis and process optimization;
- Introduction to machine learning for predictive maintenance and anomaly detection;
6. Brief overview of Robot Operating System 2 (ROS2)
- Distributed paradigm in modern robotics. - Topics, Services, and Actions.
- DDS and Quality of Service (QoS).
- Development of distributed applications in ROS2.
7. Industrial Networks and Communications
- Industrial communication models.
- Fieldbuses and industrial networks.
- CAN, Modbus, and Industrial Ethernet.
- Fieldbus system architectures and classification.
- Selection and evaluation criteria for industrial protocols.
8. IIoT Technologies and Digital Integration
- Wireless sensor networks.
- MQTT and industrial interoperability.
- Node-RED.
- Industrial databases.
- Cloud platforms for IoT.
- Visualization and analysis of industrial data.
- Development of IIoT applications for remote monitoring and supervision.
Evaluation Methodology
Continuous assessment based on three components:
1. Weekly laboratory work (50%) - This component assesses active participation and performance in practical exercises and tasks completed during laboratory classes.
2. Final project demonstration (30%) - Presentation and functional demonstration of the distributed industrial control system developed as part of the final project.
3. Final project report (20%) - Assessment of the final project's technical documentation.
The final grade is the weighted sum of these three components and must be greater than 10 points.
These assessment criteria apply to all assessment periods.
Bibliography
- Barros, M. (0). Sebenta e Slides de - Sistemas Distribuídos de Controlo (in PT). Acedido em 24 de setembro de 2015 em http://www.e-learning.ipt.pt/course/view.php?id=1020
- Barry, R. (2016). Mastering the FreeRTOSTM Real Time Kernel. (Vol. 1). (pp. 1-371). https://www.freertos.org/Documentation/RTOS_book.html: https://www.freertos.org
- Mahalik, N. (2003). Fieldbus Technology, Industrial network Standards for realtime distributed control. (Vol. 1). Springer online: Springer
- Margolis, M. (2011). Arduino Cookbook. (Vol. 1). OReilly Media online: OReilly Media
- Technologies, I. (2004). Practical Distributed Control Systems (DCS) for Engineers and Technicians. (Vol. 1). (pp. 1-623). www.idc-online.com: IDC Technologies
Teaching Method
Student-centered methodology, combining theoretical classes, laboratory activities, and project-based learning (PBL).
Students analyze, develop, and validate distributed control solutions in realistic industrial contexts.
Software used in class
Free Tools:
-SCADABR (www.scadabr.com.br/)
- FreeRTOS (https://www.freertos.org)
- Arduino IDE (https://www.arduino.cc)
- Atmel Studio (www.atmel.com/microsite/atmel_studio6/)
- Visual Studio Code (Microsoft))
- Times Tool (www.timestool.com/)
