1. Modelling, mathematical and simulation models, physical models, their application.
2. Basic forms of the mathematical models of the dynamic systems, methods of their obtaining, introduction of the analytical and experimental system identification.
3. Simulation programmes – classification, use and implemented numerical methods.
4. Modelling of the mechanical subsystems of the mechatronic systems.
5. Modelling of the electrical subsystems of the mechatronic systems.
6. Modelling of the hydraulic systems, derivation and realization of the mathematical models.
7. Modelling of the thermal systems.
8. Experimental identification using the deterministic signals. Step response approximation. Parameterization of the responses – step response, impulse response.
9. Frequency response measurement and parameterization.
10. Statistical methods of system identification. Statistic characteristic of the signals and processes.
11. System identification using the correlation methods. Stochastic form of the dynamic system – Wiener – Hopf equation. Random test signals.
12. Discrete model identification, different structures of the random signal and random process models. Model parameters obtaining, least square methods.
13. Recursive identification methods, weight coefficients, exponential weight function.
14. Identification of the systems operated in the closed loop.
2. Basic forms of the mathematical models of the dynamic systems, methods of their obtaining, introduction of the analytical and experimental system identification.
3. Simulation programmes – classification, use and implemented numerical methods.
4. Modelling of the mechanical subsystems of the mechatronic systems.
5. Modelling of the electrical subsystems of the mechatronic systems.
6. Modelling of the hydraulic systems, derivation and realization of the mathematical models.
7. Modelling of the thermal systems.
8. Experimental identification using the deterministic signals. Step response approximation. Parameterization of the responses – step response, impulse response.
9. Frequency response measurement and parameterization.
10. Statistical methods of system identification. Statistic characteristic of the signals and processes.
11. System identification using the correlation methods. Stochastic form of the dynamic system – Wiener – Hopf equation. Random test signals.
12. Discrete model identification, different structures of the random signal and random process models. Model parameters obtaining, least square methods.
13. Recursive identification methods, weight coefficients, exponential weight function.
14. Identification of the systems operated in the closed loop.