Course detail

Intelligent Systems

FIT-SINAcad. year: 2027/2028

Intelligent and distributed control systems, applications. Industry 4.0. Hierarchical control, holonic and agent-based systems. Cyber-physical systems. Components and architectures of intelligent and distributed control systems – PLC, DCS, IoT, HMI, SCADA. DCS modeling and programming – IEC 61499, Node-RED, ROS. Soft computing in control systems – fuzzy control, neural networks. Reconfigurable and agent-based control systems. Intelligent buildings, smart cities, and intelligent transportation.

Language of instruction

Czech

Number of ECTS credits

5

Assignment to study programme types

Master's

Mode of study

Not applicable.

Entry knowledge

Basics of systems theory, simulation.
Students can use any other special knowledge to implement an individual project.

Rules for evaluation and completion of the course

  • Individual project
  • Project presentation
  • To pass the exam, at least 30 points is required.

Aims

To acquaint students with principles, architectures, and methods of design of intelligent control systems.
The course is suitable for students of all specializations taught at FIT.
Ability to model and design intelligent (smart) systems and their control using current methods and technologies.
Students acquire knowledge of principles, architectures and design of smart systems of various kinds using current technologies.

Prerequisites and corequisites

Not applicable.

Basic literature

BRAMBILLA, Marco; CABOT, Jordi; WIMMER, Manuel. Model-Driven Software Engineering in Practice. 2nd ed. Springer, 2017. https://link.springer.com/book/10.1007/978-3-031-02549-5 
DAVID, René; ALLA, Hassane. Discrete, Continuous, and Hybrid Petri Nets. 2nd ed. Springer, 2010. https://link.springer.com/book/10.1007/978-3-642-10669-9
KOPETZ, Hermann; STEINER, Wilfried. Real-Time Systems: Design Principles for Distributed Embedded Applications. 3rd ed. Springer, 2022. https://link.springer.com/book/10.1007/978-3-031-11992-7 
ÅSTRÖM, Karl J.; MURRAY, Richard M. Feedback Systems: An Introduction for Scientists and Engineers. 2nd ed. Princeton University Press, 2021. https://fbsbook.org/ 
ZEIGLER, Bernard P.; MUZY, Alexandre;KOFMAN, Ernesto. Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations. 3rd ed. Academic Press / Elsevier, 2018. https://shop.elsevier.com/books/theory-of-modeling-and-simulation/zeigler/978-0-12-813370-5 
ZOITL, Alois; LEWIS, Robert. Modelling Control Systems Using IEC 61499. 2nd ed. The Institution of Engineering and Technology, 2014. https://shop.theiet.org/model-cont-syst-iec-61499-2nd 

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NISY , 2 year of study, winter semester, compulsory, profile core courses
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective
    specialization NSEC , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, elective
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  1. Introduction – Intelligent and distributed control systems, applications, Industry 4.0.
  2. Basic principles of process control. 
  3. Control system architectures: PLC, DCS, HMI, SCADA.
  4. Industrial Communication, Interoperability, and CPS.
  5. IoT principles. Edge and cloud computing. MQTT, Node-RED, and Home Assistant.
  6. Methods and tools for model-based system development.
  7. PLC programming according to IEC 61131-3.
  8. DCS modeling and programming according to IEC 61499.
  9. Hierarchical and reconfigurable systems, agent-based control.
  10. Implementation of supervisory and intelligent layers – rule-based systems, neural networks, adaptive and learning systems.
  11. Intelligent buildings, BAS technologies.
  12. Smart cities and intelligent transportation.
  13. Autonomous and robotic systems.

Project

26 hours, compulsory

Teacher / Lecturer

Syllabus

  • Individual project - implementation of distributed intelligent control in a simulated environment. The application area can be Smart Home, Transportation Systems Telematics, Smart Manufacturing, etc.