Modelling and simulation

Objectives and outcomes

Students understand and master methods and techniques in the field of computer simulation and virtual reality and use this knowledge in practical work. Students acquire the knowledge and skills needed to solve practical automation problems using computer simulation in automation and modern software in simulation. Students master the theoretical, methodological and practical knowledge of the development of computer simulations of real systems, which is further applied through the use of modern techniques, models and processes. Upon completion of the course, students will be able to understand and describe the dynamic behavior of the system, the design of business processes and the evaluation of their performances using appropriate models. Students will learn to recognise problems that can be solved by simulation modeling and will gain basic experience in the application of interactive software tools (Matlab, Simulink, GPSSH, etc.) for simulations.


Basic concepts of modeling and simulation (introduction to computer simulation; goals and purpose of simulation; simulation, models, computer experiments; computer simulation in production automation; continuous and discrete models; deterministic and stochastic simulation). Types of models. Modeling and simulations. Simulation tools: analog computer; digital computer – MATLAB (GNU Octave, version 5.1.0). Simulink: Basics of MATLAB. Simulation process. Division and types of simulation models. Model classification. Estimation of model parameters (deterministic model, statistical approach to estimation of static model parameters). Simulation of continuous systems. System simulation: a model described by algebraic equations. Simulation of discrete events. Model validation and verification. General Purpose Simulation (GPSS). Computer animation. Computer visualization. Application in science, education, business and other fields. Artificial intelligence and simulation.

Practical classes

Familiarisation with the simulator of Automatic radar target tracking. Analysis of the automatic radar target tracking simulation model. Limitations in modelling and simulation. Advantages and disadvantages of automatic radar target tracking simulation. Analysis of the parameters of this simulation model. Model validation and verification of simulations of this simulation model. Analog and digital means for this simulation. A formal model of one or more variables – a block. Kalman filter. Implementation of the simulator in Matlab and Simulink software tool. Creating event-based models. Creating activity-based models. Creating a process-based simulation model. Getting to know the DEVS simulator. Simulation in GPSS language.