Up-to-date information will be distributed via Moodle only!
Please register for the lecture via the TUCaN system of TU Darmstadt. If you have problems accessing Moodle, please contact Dr.-Ing. Eric Lenz
General information about the lecture
| Lecturer | Dr.-Ing. Eric Lenz |
| Scope | WS (2+1) |
| Prerequisites | System Dynamics and Control Systems I or Optimal and Predictive Control or Control of Distributed Cyber-Physical Systems or Model Predictive Control and Machine Learning |
| Desirable prerequisites | Fundamentals of statistical signal theory |
| Examination |
written, oral Depending on the number of exam registrations, the exam will be either written or oral. The type of examination will be announced shortly after the end of the registration period. In the winter semesters, the examination is expected to be written. |
| Old exams | Old exams can be downloaded via Moodle. |
| Supplementary and advanced courses | Modeling, Simulation and Optimization |
| Recommended literature |
Isermann, Münchhof: Identification of Dynamic Systems, Springer Verlag, 2011. Ljung, L.: System Identification: Theory for the user. Prentice Hall information and systems sciences series. Prentice Hall PTR, Upper Saddle River NJ, 2. edition, 1999. Pintelon, R.; Schoukens, J.: System Identification: A Frequency Domain Approach. IEEE Press, New York, 2001. |
Content of the lecture
Aim of this course
Introduction to the determination of mathematical models for the behavior of dynamic systems from measured signals.
Topics
- Introduction
- Basics
- Stochastics
- Signal processing (Fourier series, Fourier transform, DTFT, DTF, …)
- Stochastic and deterministic signals
- Non-parametric identification
- Frequency response estimation with periodic and time-limited signals
- Parametric identification
- Determination of parameters
- Identification via the minimization of the output error
- Identification using time and frequency domain data with the linear least squares method
- Numerical methods
- Instrumental variable method
- Identification using Kalman filters
Exam summer semester 2024
| Exam | written |
| Date | 31.07.2024 |
| Time | 09:00 – 10:30 |
| Room | S3|11-08 |
| Permitted tools |
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