Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
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227-0689-00L  System Identification

SemesterAutumn Semester 2018
LecturersR. Smith
Periodicityyearly recurring course
Language of instructionEnglish



Catalogue data

AbstractTheory and techniques for the identification of dynamic models from experimentally obtained system input-output data.
ObjectiveTo 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.
ContentIntroduction 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.
Literature"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.

"Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977.
Prerequisites / NoticeControl systems (227-0216-00L) or equivalent.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersR. Smith
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
227-0689-00 VSystem Identification2 hrs
Wed10-12HG E 1.2 »
R. Smith
227-0689-00 USystem Identification1 hrs
Wed12-13ETZ D 61.1 »
12-13HG E 1.2 »
R. Smith

Groups

No information on groups available.

Restrictions

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Offered in

ProgrammeSectionType
DAS in Data ScienceMachine Learning and Artificial IntelligenceWInformation
Data Science MasterCore ElectivesWInformation
Doctoral Dep. of Information Technology and Electrical EngineeringDoctoral and Post-Doctoral CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Robotics, Systems and Control MasterCore CoursesWInformation