SylabUZ
Course name | Smart measurement and control systems |
Course ID | 11.9-WE-AutP-SMandCS-Er |
Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
Field of study | Automatic Control and Robotics |
Education profile | academic |
Level of studies | Erasmus programme |
Beginning semester | winter term 2017/2018 |
Semester | 6 |
ECTS credits to win | 4 |
Course type | obligatory |
Teaching language | english |
Author of syllabus |
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The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Lecture | 30 | 2 | - | - | Exam |
Laboratory | 30 | 2 | - | - | Credit with grade |
SCADA systems, embedded systems, industrial automation devices, hardware control systems
Basics of intelligent measurement and control systems. Evolution of measurement and control systems. ISA reference communication model. Architecture of network measurement and control systems. Intelligent Nodes. Dedicated operating systems for measuring and control systems nodes. Characteristic features of intelligent measuring and control systems. Communication protocols of measurement and control systems. Characteristics of selected standard communication protocols: PROFIBUS, CAN, LonWorks and INTERBUS-S. Industrial Ethernet. Integration, configuration and management of measurement and control systems. Internet technologies in measurement and control systems. Dedicated web servers. Technology for creating applications and configuring dedicated web servers. Examples of dedicated web server solutions. Wireless measuring and control systems. Communication protocols of wireless measuring and control systems. Wireless sensor networks. IoT in measuring and control systems. Selected areas of application. Assessment of communication parameters. Design basics. Analysis of communication efficiency and time parameters of the designed measuring and control system. Criteria for choosing a communication protocol. Examples of measurement and control systems with distributed intelligence.
lecture: discussion, consultation, conventional lecture
laboratory: discussion, consultation, group work, project method
Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - the pass condition is to obtain a positive grade from the written exam.
Laboratory - the condition for passing is obtaining positive grades from all laboratory exercises, planned to be implemented under the laboratory program
Components of the final grade = lecture: 50% + laboratory: 50%
Modified by dr hab. inż. Wojciech Paszke, prof. UZ (last modification: 01-05-2020 10:33)