Statistical Methods for Analysis and Identification of Mechanical Systems

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Mechanics, Biomechanics and Mechatronics

Synopsis:

The student will be acquainted with the principles and procedures of statistical methods for analysis and identification of mechanical systems.

Models of dynamic systems: state models, ARX, AR, ARMAX, ARMA, OE, BJ.

Random process and its statistical characteristics

Passing random process through linear dynamic system

Signal processing, sampling, windows

Identification of linear dynamic system, experimental and operational modal analysis

Models of nonlinear dynamic systems: Markov chains, Wiener model

Passing random process through nonlinear dynamic system

Identification of nonlinear dynamic system

Stochastic identification methods: regression, correlation, adaptive methods

Identification of nonlinear systems: neuro-fuzzy methods (LOLIMOT), neural networks.

Kalman filter for nonlinear systems

Identification in closed loop control. Identification of unstable systems.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Julius S. Bendat, Allan G. Piersol: Engineering Applications of Correlation and Spectral Analysis, J. Wiley 2013

Rolf Isermann, Marco Münchhof : Identification of Dynamic Systems: An Introduction with Applications, Springer 2010

L. Ljung: System Identification - Theory for User, Prentice Hall PTR, 1999

Štefan, M.; Šika, Z.; Valášek, M.; Bauma, V.: Neuro-Fuzzy Identification of Nonlinear Dynamic MIMO Systems

Engineering mechanics. 2006, 13(3), pp. 223-238.

odkaz: https://moodle-vyuka.cvut.cz/

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: