Course Information

Course Name	Turkish	Sistem Tanıma
	English	System Identification
Course Code	KOM 512	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		Turkish
Course Coordinator		Yaprak Yalçın
Course Objectives		• To understand the important of modeling concept, types of models , mathematical modeling and system identification for control science and engineering • To teach nonparametric methods in system identification; transient, frequency, correlation and spectral analysis • To teach parametric methods in system identification; linear regression, the least squares estimation, the best linear unbiased estimate • To understand the properties of input signals which are used for a system identification experiment and the concept of persistent excitation • To teach model structure and model estimation • To teach prediction error methods, instrumental variable methods, recursive system identification methods, system identification in closed loop • To teach model validation and model structure determination
Course Description		Modeling concept, types of models and examples. Comparison of mathematical modeling and system identification. Flowchart of system identification, basic definitions and concepts. Nonparametric methods in system identification; transient, frequency, correlation and spectral analysis. Parametric methods in system identification; linear regression, the least squares estimation, the best linear unbiased estimate. Input signals and persistent excitation. Model estimation. Prediction error methods, instrumental variable methods. Recursive system identification methods, system identification in closed loop. Model validation and model structure determination
Course Outcomes		• To be able to know the important of modeling concept, types of models , mathematical modeling and system identification for control science and engineering • To be able to use nonparametric methods in system identification; transient, frequency, correlation and spectral analysis • To be able to use parametric methods in system identification; linear regression, the least squares estimation, the best linear unbiased estimate • To be able to choose the input signals which are used for a system identification experiment and to know the concept of persistent excitation • To be able to choose the model structure and to do model estimation • To be able to use prediction error methods, instrumental variable methods, recursive system identification methods, system identification in closed
Pre-requisite(s)
Required Facilities
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Textbook		• I. D. Landau, System Identification and Control Design, Prentice-Hall, 1990. • V. Söderström, P. Stoica, System Identification, Prentice Hall, 1989. • L. Ljung, System Identification: Theory for the User, Prentice-Hall, 1987. • P. E. Wellstead, M. B. Zarrop, Self-Tuning Systems, John Wiley and Sons, 1995
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