Computer simulation

Higher education teachers: Karer Gorazd
Credits: 6
Semester: summer
Subject code: 64133

Subject description


  • Enrolment in the 3rd year of study

Content (Syllabus outline):

Introduction: definitions, modelling and simulation as a unified cyclic procedure, applicability in the field of control systems. Basics of modelling: balance equations, theoretical and experimental modelling, examples. Types of models and simulations: continuous, discrete-event, hybrid, simulation in real time. Simulation methods: indirect and implicit method, simulation of transfer functions - nested and partitioned method, simulation systems with large delays. Simulation tools: the basic features. Simulation using the basic functions of Matlab environment. Simulation in Matlab - Simulink: basic capabilities, advanced capabilities: subsystems and masking, conditionally executable systems, analysis and optimization of Simulink models: execution of Simulink models from Matlab, linearization, steady state analysis, optimization, S- functions. Multi-domain, object-oriented modelling: causal and non-causal models, important properties of OO environments. Language Modelica, a standard library, Modelica environments, Dymola environment. How digital simulation works. Numerical integration, sorting algorithm, simulation using general purpose programming languages. Numerical methods and problems: integration methods, numerical stability, the problem of discontinuities, the problem of algebraic loops. Simulation of discrete-event systems. The strategy of the triggering with event graphs and process flows, examples with Matlab, SimEvents, Enterprise Dynamics, AnyLogic. Statistical features in discrete event modelling and simulation. Engineering approach in experimental modelling. Experimental modelling of proportional and integral processes. Engineering understanding of the responses and simplified models.

Objectives and competences:

Computer simulation is the most important, the most common but also relatively simple approach for the analysis and design of systems, also control systems. The basic objective is to present areas in an interesting way through a series of examples and using computer tools. Students will learn the basic approaches to modelling of continuous systems as well as discrete event systems, the basic approaches to simulation, they learn the basic and advanced capabilities of computer tools and become familiar with numerical problems in digital simulation.

Intended learning outcomes:

  • Profound knowledge of modelling and simulation methods, profound knowledge of the use of advanced computer tools: Matlab, Simulink, Dymola-Modelica.

Learning and teaching methods:

  • Lectures and laboratory exercises.

Study materials


  1. J.B. Dabney, T.L. Harman , Mastering SIMULINK , Prentice Hall, Upper Saddle River, N.J., USA, 2004.


  1. D. Matko, B. Zupančič, R. Karba , Simulation and Modelling of Continuous Systems - A Case Study Approach, Prentice Hall, 1992.
  2. Dymola, Dynamic Modeling Laboratory, Users manual, ver 2014 FD01. Dessault Systems, Dynasim AB, Sweden, Lund, 2013.
  3. R. Karba, Modeliranje procesov, 1. izdaja, Univerza v Ljubljani, Fakulteta za elektrotehniko, 1999.
  4. F.E. Cellier, Continuous System Modeling, Springer - Verlag, NY, USA, 1991.
  5. F.E. Cellier, E. Kofman, Continuous System Simulation, Springer Science+Business Media, Inc., NY, USA, 2006
  6. P. Fritzson, Principles of Object Oriented Modeling and Simulation with Modelica 2.1, IEEE Press, John Wiley&Sons, Inc., Publication, USA, 2004
  7. S. Raczynski, Modeling and Simulation, John Wiley & Sons, Ltd., England, 2006

Study in which the course is carried out

  • 3 year - 1st cycle - Electrical Enginnering - Control Engineering