This course is about non-parametric system identification based on estimators of spectral densities and its application to open-loop and closed-loop systems. Furthermore parameter estimation for linear and non-linear systems playes an important role. At the end of the course, a choice can be made out of three final assignments, for which recorded signals are available. The available demonstration programs have to be adapted in order to estimate proper transfer functions and model parameters.

1 design test signals to identify an unknown system; a. design proper experimental measurement conditions; b. understand the differences between stochastic and deterministic signals; c. indicate the differences in application between transient and continuous signals.

2 estimate a nonparametric model of the unknown system from recorded signals; a. recognize and identify open-loop and closed-loop relations between measured signals; b. employ proper techniques to identify models in the frequency and time domain; c. validate the nonparametric models using different indicators; 3 parameterize nonparametric models; a. derive the best model structure based on a priori knowledge from physics; b. parameterize the dynamic relation between the recorded signals using linear and non-linear parameter estimation techniques; c. implement different optimization techniques d. assess the uniqueness of the parameters using correlation analysis; e. evaluate the derived parameterized model through validation techniques; f. recognize three non-linear model structures, and their applicability in a given situation

Creative Commons License
System Identification and Parameter Estimation by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at
Back to top