Systems and Models

The course is a substitute for:

Systems and Models (E35SAM)
Systems and Models (X35SAM)

Lecturer:

Tutor:

Supervisor:

Department of Control Engineering

Synopsis:

Dynamical systems modeling and identification of deterministic models assumed to be used in simulation, analysis and control system design namely in automation. Bond Graph systematic modeling of mechanical, hydraulic, pneumatic, electrical and thermal systems and their assemblies. State space and Input-Output formal models, e.g. transfer functions, differential equations, frequency and step response. Continuous and discrete time models. Spectral analysis and least squares methods for nonparametric and ARX model identification.

Requirements:

Complex numbers, differential and difference calculus basics, Laplace transform, frequency response, matrix calculus basics

Syllabus of lectures:

1. Systems, control systems modeling

2. Input-Output models of continuous time, transfer functions, model analysis

3. State space continuous time models, model analysis, linearization

4. Analytical and numerical methods for solving state space equations

5. State space and input-output discrete time models, linear model analysis

6. Impulse, step and frequency response characteristics

7. Sampling of continuous signals and system, sampling rate selection

8. Modeling of physical systems by Bond Graphs

9. Modeling of electromechanical, hydraulic and pneumatic systems

10. Modeling of sensors, amplifiers and power transducers

11. Thermal systems modeling

12. Formal model conversion and model order reduction

13. Signal based system identification, spectral analysis and least squares methods

14. Design of experiment for identification of ARX model parameters from data

Syllabus of tutorials:

1. Introduction to laboratory equipment, scale models of physical systems

2. MATLAB programming package - elementary functions

3. SIMULINK system simulation package

4. Control and Signal Toolboxes for MATLAB

5. Data acquisition and real-time signal processing in Real Time Toolbox for MATLAB

6. Laboratory project 1: modeling, parameter identification by direct measurement

7. Simulation of nonlinear continuous time model in SIMULINK

8. Linearization and simulation of the linearized model, comparison with nonlinear model

9. Bond Graph modeling, Z-transform, discrete systems descriptions

10. TEST, continuous to discrete time model conversion, individual project definition

11. Laboratory project 2: modeling, parameter identification by direct measurement

12. Simulation of nonlinear and linearized continuous time model in SIMULINK

13. Bond Graph model of the identified system

14. Discussion on laboratory project results

Study Objective:

Study materials:

1. Karnoop, D.C., Margolis, D.L., Rosenberg, R.C.: System Dynamics: A Unified Approach. John Wiley, New York 1990

2. Franklin, G.F., Powell, J.D., Emami-Naeini, A.: Feedback Control of Dynamic Systems. Addison-Wesley, New York 1995

3. Astrom, K.J., Wittenmark, B.: Computer Controlled Systems - Theory and Design. Prentice Hall, Englewood Cliffs 1990

4. Ljung, L., Torkel, G.: Modeling of Dynamic Systems. Prentice Hall, Englewood Cliffs 1994

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Společný plán- strukturované anglické studium (elective specialized course)
• Electronics and Communication Technology - structured studies (elective specialized course)
• Cybernetics and Measurements- structured studies (compulsory course, elective specialized course)
• Heavy-current Engineering- structured studies (elective specialized course)
• Computer Technology- structured studies (elective specialized course)