Course detail

Real Time Control and Simulation

FSI-RPOAcad. year: 2017/2018

Students will learn about advanced techniques of real-time simulations, identification, advanced control systems and state/parameter estimation. Theoretical findings will be applied on team project dealing with complex control design for real educational model.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. Robert Grepl, Ph.D.

Department

Institute of Solid Mechanics, Mechatronics and Biomechanics (ÚMTMB)

Learning outcomes of the course unit

Students will gain knowledge about
• rapid control prototyping and HIL
• system identification
• state space control
• Kalman filter
• nonlinear control
• complex team project.

Prerequisites

Knowledge from modules: RMW, RDO, RKD.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Lectures, labs.

Assesment methods and criteria linked to learning outcomes

Module is graded according to:
• active participation on exercises/labs
• project
• tests.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Students will learn about advanced techniques of real-time simulations and related SW and HW. Theoretical findings will be demonstrated on process of identification and design of advanced control system for real laboratory model.

Specification of controlled education, way of implementation and compensation for absences

Attendance at practical training is obligatory. Evaluation are made on exercises based on evaluation criteria.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

BOLTON, W. Mechatronics: Electronic Control Systems in Mechanical Engineering. Pearson Education, 1999. 372 p. ISBN: 9780582357051.
Grepl, R.: Modelování mechatronických systémů v Matlab/SimMechanics, BEN - technická literatura, ISBN 978-80-7300-226-8
NELLES, O. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, 2000-12-12. 814 p. ISBN: 9783540673699.
Valášek, M.: Mechatronika, skriptum ČVUT, 1995

Recommended reading

NELLES, O. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, 2000-12-12. 814 p. ISBN: 9783540673699.
Valášek, M.: Mechatronika, skriptum ČVUT, 1995

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MET , 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Robert Grepl, Ph.D.

Syllabus

Dynamic behaviour and properties of drive systems.
Structure of drive systems.
Interactive drive systems.
Basic drive systems: machines, gearbox - industry machines.
Basic drive systems: machines, gearbox - industry machines.
Operating states of drive systems and their stability.
Operating states of drive systems and their stability.
Computational modelling of drive systems.
Computational modelling of drive systems.
Stability of drive systems and defects.
Experimental monitoring of drive systems dynamics properties.