Course detail

Simulation of Dynamic Systems

FSI-FSIAcad. year: 2020/2021

Modeling and Simulation is a discipline for developing a level of understanding of the interaction of the parts of a system, and of the system as a whole. The course is focused to continues, discrete and hybrid simulation. Modeling and Simulating by means of finite automata, cellular automata and Petri nets. Modeling and simulating complex reactive systems. Random Number Generation and Monte Carlo Methods. Design of simulation experiments, HIL simulation, visualization and verification.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Learning outcomes of the course unit

Students will be able to use software methods and applications for simulation.


Fundamentals of mathematics, including differential and integral calculus of functions in one and more variables and solution of system differential equations. Fundamentals of physics, mechanics, electrical engineering and automatic control, knowledge of programming techniques in environment C/C++ and Matlab.


Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.

Assesment methods and criteria linked to learning outcomes

Course-unit credit is conditional on completing of computer projects. Exam has a written and an oral part and tests students’ knowledge of the subject-matter covered in the course.

Course curriculum

Not applicable.

Work placements

Not applicable.


The aim of the course is to make students familiar with the methods and selected software supporting the computer simulation.

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

Attendance at seminars is checked by means of projects.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Ross S.: Simulation, 3rd edition, Academic Press, 2002
Zeigler B., Praehofer H., Kim T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Fishwick P.: Simulation Model Design and Execution, Building Digital Worlds, Prentice-Hall, 1995

Recommended reading

Zeigler B., Praehofer H., Kim T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Šťastný, J.: Simulace systémů. Elektronická studijní opora, VUT Brno, 2002


Classification of course in study plans

  • Programme B3S-P Bachelor's

    branch B-AIŘ , 3. year of study, winter semester, compulsory

  • Programme M2I-P Master's

    branch M-AIŘ , 2. year of study, winter semester, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


P1: Tools for computer simulation.
P2: Basics of system theory. Classification of models.
P3: Formal systems.
P4: Petri's nets and finite automaton. Modeling of parallel processes.
P5: Cellular automatons and their use for simulation.
P6: Agent based modeling.
P7: Modeling and simulation of continuous systems I (description, types and solution).
P8: Modeling and simulation of continuous systems II (ODE solvers, error and stability).
P9: Modeling and simulation of continuous and discrete systems.
P10: Modeling and simulation of event driven systems.
P11: Modeling and simulation of stochastic systems. Method Monte Carlo.
P12-13: Modern trends in simulation (HIL simulation, dSpace tools for simulations in automobiles and aeronautics).

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer


Computer labs (exercises) are consistent with the content of lectures. The aim of the labs is to introduce students to practical part of the course above all using Matlab/Simulink system.
The labs are divided into nine parts (modeling and simulation):
a) Mechanical systems
b) Electromechanical systems
c) Logical and electronic systems
d) Fluid systems
e) Stochastic systems (pseudorandom number generators)
f) Cellular automata. Agent based systems.
g) Event driven systems.
h) Petri nets.
i) Fuzzy models.