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227-0689-00L 4 Credits DR , MSC , WBZ D-ITET , D-MATH , D-INFK , D-MAVT , D-ERDW , D-PHYS

System Identification

Lecturers & Examiners: Prof. em. Dr. Roy Smith

VVZ CR n/a

Last Updated: 2026-06-01 11:30:52

Abstract

Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data.

Objective

To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.

Content

Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models. Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods. Optimal experimental design, Cramer-Rao bounds, input signal design. Parametric identification methods. On-line and batch approaches. Closed-loop identification strategies. Trade-off between controller performance and information available for identification.

Resources

Literature

"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999. Additional papers will be available via the course Moodle.

General Information

Language: English
Levels: DR , MSC , WBZ

Examination

Type: graded semester performance

Final project (50%)Final written examination (50%).The written examination will be held on Wednesday 18th February, 14:00 - 17:00.

Course Components

Type	Title	Time & Place	Hours
lecture with exercise	System Identification Will be offered as a block course. Dates: 26.01.26, 27.01.26, 28.01.26, 29.01.26, 30.01.26, 02.02.26, 03.02.26, 04.02.26, 05.02.26, 06.02.26 Times: Lecture 9:00 - 10:30 / 14:00- 15:30, Exercise 10:45 - 12:15 / 15:45 - 17:15	26.01. - 06.02 Date 09:15-13:00 (LFO C 13) 26.01. - 06.02 Date 14:15-18:00 (LFO C 13)	53 h semesterly

Offered In

Maschineningenieurwissenschaften Master
- Kernfächer
Elektrotechnik und Informationstechnologie Master
- 01 - Fächer der Vertiefungen
  - Vertiefung: Systems and Control (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Vertiefungsfächer (These specialisation courses are particularly recommended for the area of "Systems and Control", but you are free to choose courses from any other field in agreement with your tutor. Semester / Research Projects are not allowed in this category. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.)
Robotics, Systems and Control Master
- Kernfächer
Doktorat Informationstechnologie und Elektrotechnik (A minimum of 12 ECTS credit points must be obtained during doctoral studies (also see sub-categories for details) More Information at )
- Vertiefung Fachwissen (The courses on offer below are only a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.)
Data Science Master
- Master-Studium (Studienreglement 2023)
  - Wahlfächer
    - Fachspezifische Wahlfächer
- Master-Studium (Studienreglement 2017)
  - Kernfächer
    - Wählbare Kernfächer
DAS in Data Science
- Vertiefungen
  - Machine Learning and Artificial Intelligence
Space Systems Master
- Fachspezifische Vertiefung (Es müssen mindestens 20 KP aus den Deep Track Lerneinheiten absolviert werden. Überzählige KP können für Wahlfächer angerechnet werden.)
  - Robotics
    - Vertiefungsfächer Robotics (Diese LE's können sowohl als Vertiefungsfach als auch als Wahlfach angerechnet werden.)