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Lecturer(s)
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Kubalčík Marek, prof. Ing. Ph.D.
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Course content
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-Classification of dynamic stochastic regression models suitable for design of modern control methods. - Linear dynamic stochastic models (ARX, ARMAX, ARIMAX, OE, BJ, state space). - Least squares method - Extended least squares method - Instrumental variable method - Prediction error method - Recursive identification algorithms. - Nonlinear dynamic stochastic models (NARX, NARMAX, NOE). - Hammerstein and Wiener model structures and their identification. - Utilization of artificial intelligence methods for identification of nonlinear systems - Evaluation of quality identification experiments.
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Learning activities and teaching methods
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Methods for working with texts (Textbook, book), Exercises on PC
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| prerequisite |
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| Knowledge |
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| Adaptive and predictive control (Master's course) System Identification (Master's course) |
| Adaptive and predictive control (Master's course) System Identification (Master's course) |
| learning outcomes |
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| Classification of dynamic stochastic regression models suitable for design of modern control methods |
| Classification of dynamic stochastic regression models suitable for design of modern control methods |
| Linear dynamic stochastic models (ARX, ARMAX, ARIMAX, OE, BJ, state space) |
| Linear dynamic stochastic models (ARX, ARMAX, ARIMAX, OE, BJ, state space) |
| Modifications of the recursive least squares method |
| Modifications of the recursive least squares method |
| Nonlinear dynamic stochastic models (NARX, NARMAX, NOE) |
| Nonlinear dynamic stochastic models (NARX, NARMAX, NOE) |
| Extended least squares method |
| Extended least squares method |
| Skills |
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| Utilization of artificial intelligence methods for identification of nonlinear systems |
| Utilization of artificial intelligence methods for identification of nonlinear systems |
| Evaluation of quality of identification experiments |
| Evaluation of quality of identification experiments |
| Hammerstein and Wiener model identification |
| Hammerstein and Wiener model identification |
| Implementation of recursive identification aalgorithms |
| Implementation of recursive identification aalgorithms |
| Implementation of prediction error method |
| Implementation of prediction error method |
| teaching methods |
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| Knowledge |
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| Exercises on PC |
| Exercises on PC |
| Methods for working with texts (Textbook, book) |
| Methods for working with texts (Textbook, book) |
| assessment methods |
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| Oral examination |
| Oral examination |
| Written examination |
| Written examination |
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Recommended literature
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Digital self-tuning controllers : algorithms, implementation and applications. London : Springer, 2005. ISBN 1-85233-980-2.
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Bobál, Vladimír. Identifikace systémů. Vyd. 1. Zlín : Univerzita Tomáše Bati ve Zlíně, 2009. ISBN 978-80-7318-888-7.
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DUNÍK, J. Identifikace systémů a filtrace. ZČU v Plzni, 2018. ISBN 978-80-261-0775-0.
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Garnier, H., Wang, L. Identification. of Continuos-time Models from Sampled Data. Springer-Verlag, London, 2008. ISBN 978-1-84800-160-2.
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HOFREITER, M. Identifikace systémů I.. ČVUT v Praze, 2009. ISBN 978-80-01-04228-1.
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Janczak, A. Identification of Nonlinear SystemsUsing Neural Networks and Polynomial Models. Springer-Verlag Berlin, 2005. ISBN 3-540-23185-4.
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Keesman, K. J. System Identification. An Introduction.. Springer-Verlag London, 2011. ISBN 978-0-85729-521-7.
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Ljung, L. System identification: Theory for the user. MIT Press Cambridge, 1987.
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Mikleš, J., Fikar, M. Process modelling, identification, and control 2. Bratislava : STU, 2004. ISBN 80-227-2132-8.
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Nelles, Oliver. Nonlinear system identification : from classical approaches to neural networks and fuzzy models. Berlin : Springer, 2001. ISBN 3-540-67369-5.
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Noskievič, P. Modelování a identifikace systémů. Montanex, Ostrava, 1999. ISBN 80-7225-030-2.
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Söderström, T., Stoica, P. System Identification. Prentice Hall, University Press, Cambridge, UK, 1989.
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VROŽINA, M., JANČÍKOVÁ, Z., A DAVID, J. Identifikace systémů. VŠB - TU Ostrava, 2012. ISBN 978-80-248-2594-6.
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