SylabUZ
Nazwa przedmiotu | System modelling and identification |
Kod przedmiotu | 11.9-WE-AutD-SysModeliIdentyf.-Er |
Wydział | Wydział Nauk Inżynieryjno-Technicznych |
Kierunek | Automatyka i robotyka |
Profil | ogólnoakademicki |
Rodzaj studiów | Program Erasmus drugiego stopnia |
Semestr rozpoczęcia | semestr zimowy 2023/2024 |
Semestr | 1 |
Liczba punktów ECTS do zdobycia | 6 |
Typ przedmiotu | obowiązkowy |
Język nauczania | angielski |
Sylabus opracował |
|
Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 30 | 2 | - | - | Egzamin |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
To provide fundamental knowledge in system identification, including: input signal selection, model order selection, non-recursive and recursive identification methods.
To develop skills in building system models based on structure knowledge and input-output measurements, including nonparametric identification methods, parametric identification methods, neural networks and fuzzy models.
Signals and dynamic systems, Control engineering, Artificial intelligence methods
Introduction. Plants and their models. Model using. System identification and mathematical modelling. Equivalence of models and model equivalence criteria. Parameter estimation. Identifications error definitions. Building system models based on structure knowledge and measurements. Identification algorithm scheme.
Nonparametric identification methods. Transient states analysis. Frequency identification methods. Correlation methods. Power spectrum analysis.
Least squares method. Linear static models. Least squares problem. Normal equations. Analysis of least squares estimator. Best linear unbiased estimator. Confidence intervals of parameter estimates. Model complexity. Finding the least squares solution with orthogonal-triangular decomposition. Recursive least squares algorithm.
Models of dynamic systems. Model classification. General structure of linear model. AR, AR, MA, ARMA, FIR, ARX, ARMAX, OE, and Box-Jenkins models. Multi-input multi-output models. Nonlinear models. Wiener and Hammerstein models. Volterra and Kolmogorov-Gabor models. State-space models. Model structure selection.
Input signals. Deterministic signals. Stochastic signals. Input signals used in system identification. Persistent excitation condition.
Prediction error method. Simulation and prediction. Optimal predictors. Least-squares estimation of ARX model parameters. Parameter consistency problem. Instrumental variables method. Choice of instrumental variables. Prediction error method.
Recursive identification. Properties of recursive identification algorithms. Recursive least squares method. Exponential forgetting. Recursive instrumental variables method. Recursive prediction error method. Parameter adaptation of self-tuning controller.
Closed-loop identification. Identifiability of closed-loop systems. Direct identification methods. Indirect identification methods. Influence of feedback loop on estimation accuracy.
Subspace identification methods. Deterministic and stochastic state-space models. Deterministic system identification. Stochastic system identification.
Lecture, laboratory exercises.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Lecture – the main condition to get a pass are sufficient marks in written or oral tests conducted at least once per semester.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%
1. Keesman K. J.: System Identification: An Introduction. Springer, London, 2011
2. Ljung L.: System Identification. Theory for the User. Prentice Hall, Upper Saddle River, 1999
3. Nelles O.: Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, New York, Berlin, Heidelberg, 2001
4. Soderstrom T., Stoica P.: System Identification. Prentice Hall, Upper Saddle River, 1994
1. Norgaard M., Ravn O., Poulsen N.K., Hansen L.K.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London, 2000
Zmodyfikowane przez dr hab. inż. Wojciech Paszke, prof. UZ (ostatnia modyfikacja: 13-06-2023 11:55)