Course detail

Real Time Control and Simulation

FSI-RPOAcad. year: 2025/2026

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

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Robert Grepl, Ph.D.

Department

Institute of Solid Mechanics, Mechatronics and Biomechanics (ÚMTMB)

Entry knowledge

Knowledge of mathematics, kinematics, dynamics equal to previous studies and programming in MATLAB/Simulink.

Rules for evaluation and completion of the course

The course is graded on a standard 0-100 point scale. Students may earn up to 25 points for laboratory work, subject to completing at least 4 of the 7 assignments. Graded credit is awarded for a maximum of 75 points. Active participation in the labs is expected and attendance is mandatory. Learning is monitored on the basis of set assessment criteria.

Aims

The course focuses on advanced real-time simulation techniques and related software and hardware. Theoretical knowledge will be applied in laboratory exercises where students will learn the process of identifying and designing advanced controls for a real laboratory model.

Upon completion of the course, students will gain knowledge and skills in the following areas:

  • Rapid prototyping of control systems and HIL (principles, software tools and hardware).
  • System identification
  • State control
  • Kalman filter
  • Nonlinear control
  • Complex team project development

Study aids

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 N-MET-P Master's 1 year of study, summer semester, compulsory

  • Programme C-AKR-P Lifelong learning

    specialization CLS , 1 year of study, summer semester, elective

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.
Linear, nonlinear and quadratic programming.

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Robert Grepl, Ph.D.

Syllabus

Dynamics of rotating bodies.
Examples of drive systems structual analyses.
Basic features of torsion systems - examples.
Machines characteristics - examples.
Dynamics of gearbox systems - examples.
Dynamic properties modelling of industry machines.
Examples of drive systems control.
Computational modelling of movement systems.
Computational modelling of movement systems.
Stability of drive systems - examples.
Graded course-unit credit.