System identification. Laboratory exercises

Joanna Ziętkiewicz

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ISBN/ISSN:

978-83-7775-589-1

DOI:

Wydawnictwo:

Wydawnictwo Politechniki Poznańskiej

Rok wydania:

2020

Liczba stron:

XML:ISBN/ISSN:

ONIX MARC21

Bibliografia:

Ziętkiewicz, Joanna. System identification. Laboratory exercises. Red. . : Wydawnictwo Politechniki Poznańskiej, 2020, 65 s. ISBN 978-83-7775-589-1

Bibliografia refWorks:

RT Book, Whole

SR Electronic(1)

A1 Ziętkiewicz, J.

T1 System identification. Laboratory exercises

PB Wydawnictwo Politechniki Poznańskiej

YR 2020

SN 978-83-7775-589-1

Bibliografia BibTex:

@Book{ 240057, author = "Ziętkiewicz, Joanna", editor = "", title = "System identification. Laboratory exercises", publisher = "Wydawnictwo Politechniki Poznańskiej", year = "2020", address = "", isbn = "978-83-7775-589-1" }

Bibliografia endNote:

TY - BOOK

AU - Ziętkiewicz, Joanna

ED -

TI - System identification. Laboratory exercises

PB - Wydawnictwo Politechniki Poznańskiej

CY -

PY - 2020

SN - 978-83-7775-589-1

ER -

Netografia (standard APA):

Ziętkiewicz, Joanna. System identification. Laboratory exercises [baza danych online] : Wydawnictwo Politechniki Poznańskiej, 2020 [dostęp: 03 07 2024]. Dostęp w Ibuk Libra: https://libra.ibuk.pl/reader/system-identification-laboratory-exercises-joanna-zietkiewicz-240057.

automatyka, obiekty sterowania

Skrypt powstał na potrzeby ćwiczeń laboratoryjnych z przedmiotu identyfikacja obiektów ste-rowania na Wydziale Elektrycznym Politechniki Poznańskiej. Skrypt jest uzupełnieniem pod-ręcznika Identyfikacja obiektów sterowania, metody dyskretne, param...

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ISBN/ISSN:

978-83-7775-589-1

DOI:

Wydawnictwo:

Wydawnictwo Politechniki Poznańskiej

Rok wydania:

2020

Liczba stron:

XML:ISBN/ISSN:

ONIX MARC21

Preface8
1. Continuous-time and discrete-time models 10
1.1. Basic information10
1.1.1. Models10
1.1.2. Laurent transform10
1.1.3. Discrete-time models11
1.1.4. Discretisation of continuous-time models13
1.2. Exercises and the report14
1.2.1. Calculations for discrete-time and continuous-time models14
1.2.2. Simulation with continuous-time and discrete-time models15
1.2.3. Discretisation of discrete-time models15
2. Signals in system identification17
2.1. Basic information17
2.1.1. Common signals in system identification17
2.1.2. Statistical characteristics of a signal17
2.1.3. The order of excitation20
2.2. Exercises and the report21
2.2.1. Basic signal characteristics21
2.2.2. The order of persistent excitation21
2.2.3. The order of persistent excitation – calculations22
3. Nonparametric methods – transient and frequency analysis 23
3.1. Basic information23
3.1.1. Transient response identification23
3.1.2. Determining the transfer function from the step response23
3.1.3. Frequency analysis29
3.2. Exercises and the report31
3.2.1. System identification using step responses31
3.2.2. Frequency analysis31
4. Nonparametric methods – correlation and spectral analysis32
4.1. Basic information32
4.1.1. Impulse response model32
4.1.2. Correlation analysis32
4.1.3. Spectral analysis33
4.2. Exercises and the report34
4.2.1. Correlation and spectral analysis34
4.2.2. FIR model34
5. Deterministic models and the least-squares method35
5.1. Basic information35
5.1.1. Deterministic models in parametric system identification35
5.1.2. Least-squares method37
5.1.3. Influence of the persistent excitation order on parameter estimation37
5.2. Exercises and the report38
5.2.1. Parameter estimation for a system without a delay38
5.2.2. Parameter estimation for a system with a delay38
5.2.3. Influence of the excitation order on the estimation39
6. Stochastic models and the least-squares method 40
6.1. Basic information40
6.1.1. Elementary stochastic models in system identification40
6.1.2. Least-squares method for the ARX model41
6.1.3. Evaluation of the system identification result42
6.2. Exercises and the report43
6.2.1. Parameter estimation for the ARX model43
6.2.2. Evaluation of the results44
7. Model order estimation 45
7.1. Basic information45
7.1.1. Estimation of the order45
7.2. Exercises and the report46
7.2.1. Estimation of the order in a case without disturbance46
7.2.2. Estimation of the order in the presence of disturbance47
8. Instrumental variables method 48
8.1. Basic information48
8.1.1. ARMAX and OE models48
8.1.2. Instrumental variables method49
8.2. Exercises and the report50
8.2.1. Comparison of the least-squares and instrumental variables methods50
8.2.2. Instrumental variables method and the order estimation51
9. System identification in a closed loop 52
9.1. Basic information52
9.1.1. Problem of system identification in a closed loop52
9.1.2. Conditions for identifiability in a closed loop52
9.2. Exercises and the report53
9.1.3. Solution to the problem53
9.2.1. Identifiability conditions for the system in a closed loop53
9.2.2. Solution to the problem of unidentifiability54
10. Recursive estimation methods 55
10.1. Basic information55
10.1.1. Recursive least-squares method55
10.1.2. Recursive instrumental variables method56
10.1.3. Recursive extended least-squares method56
10.2. Exercises and the report57
10.2.1. The choice of the method matching the system57
10.2.2. Recursive identification58
Matlab commands59
References64