System Identification and Data Analysis: An Introduction

System Identification and Data Analysis: An Introduction. Seminar of two hours for the Nanjing University of Aeronautics and Astronautics - NUAA

Abstract

This talk provides a comprehensive and coherent description of the theory, methodology and practice of System Identification and Data Analysis, which represents the science of building mathematical models of dynamic systems by observing input/output data. It puts the user in focus, giving the necessary background to understand the theoretical foundation and emphasising the practical aspects of the options and choices that face the user.

System identification, data analysis, identification models, identification experiment, model validation, structure selection.

General Readings. Preliminaries - Introduction to modelling and system identification. (i) Theory and experiment-based modelling methods; (ii) Parametric and non-parametric models and identification methods; (iii) Procedure of system identification. Reading: L. Ljung, From Data to Model: A Guided Tour of System Identification. Linkoping University, Sweden, Report No. LiTH-ISY-R-1652, 1994.
Lecture's Slides. Non-recursive (off-line) methods. (i) Least-Squares (LS) method and its variants; (ii) models for system identification; (iii) validation approaches. Lecture Notes: seminar's slides in PDF format, 3.7MB. Offline recorded movie (UNIFE YouTube Channel, 56 mins).
Open Problems and Future Works. Some System Identification Challenges and Approaches. Slides in PDF format.

Identification Dataset Example for the System Identification Toolbox of Matlab. Example process data from a SISO model: (Dynamic process data file in ascii format, Matlab Dataset).
22. K. Sigmon, "Matlab Primer Third Edition". University of Florida, Florida, Second Edition ed., 1992. (file in formato pdf ).
Get Started with System Identification Toolbox System Identification Graphical User Interface for Matlab. (Matlab website, English).
System Identification Toolbox System Identification Graphical User Interface for Matlab. (pdf format file).

SID, "System Identification Toolbox". System Identification Graphical User Interface for Matlab (pdf format file).
System Identification: Theory for the User, Lennart Ljung - Springer, 1999.
Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, S. Simani, C. Fantuzzi, R. J. Patton - Springer, 2003.
System Identification, T. Soderstrom, P. Stoica, Prentice Hall International, Cambridge, 1989

Ljung, L. (1999). System Identification: Theory for the User (2nd ed.). Prentice Hall, Upper Saddle River, NJ, USA.
Ljung, L., Glad, T. (2021). Modeling and Identification of Dynamic Systems. Studentlitteratur AB, Lund, Sweden.
Soderstrom, T., Stoica, P. (1989). System Identification. Prentice Hall International, Hemel Hempstead, UK.
Sintelon, R., Schoukens, J. (2012). System Identification: A Frequency Domain Approach (2nd ed.). Wiley-IEEE Press, Hoboken, NJ, USA.
Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks. Springer, Berlin, Germany.
Goodwin, G.C., Payne, R.L. (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press, New York, NY, USA.

Schoukens, J., Ljung, L. (2019). Nonlinear system identification: A user-oriented roadmap. IEEE Control Systems Magazine, 39(6), 28 - 99.
Pillonetto, G., Ljung, L., Chen, T. (2023). Deep networks for system identification: A survey. Annual Reviews in Control, 56, 242 - 264.
Wahlberg, B., Ljung, L. (2018). Algorithms and performance analysis for stochastic Wiener system identification. Automatica, 95, 277 - 289.
Brunton, S.L., Proctor, J.L., Kutz, J.N. (2016). Sparse Identification of Nonlinear Dynamical Systems with Control (SINDYc). IFAC-PapersOnLine, 49(18), 710 - 715.
Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L. (2014). Kernel methods in system identification, machine learning and function estimation: A survey. Automatica, 50(3), 657 - 682.
Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1 - 12.
Verdult, V., Ljung, L. (2005). Nonlinear state-space system identification with application to neural networks. Automatica, 41(11), 1771 - 1784.
Ljung, L. (2008). Perspectives on the relation between artificial intelligence and system identification. IFAC Proceedings Volumes, 41(2), 1 - 12.
Gevers, M., Ljung, L. (1986). Optimal experiment designs with respect to the intended model application. Automatica, 22(5), 543 - 554.

Ljung, L. (2024). System Identification Toolbox (version 10.1). MathWorks, Natick, MA, USA. (PDF format file).