Course*  System Identification and Parameter Estimation
This course is about nonparametric system identification based on estimators of spectral densities and its application to openloop and closedloop systems. Furthermore parameter estimation for linear and nonlinear 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. Study Goals: The student will be able to: 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 openloop and closedloop 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 nonlinear 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 nonlinear model structures, and their applicability in a given situation.

Lecture 1  Introduction

Lecture 2  Correlation Functions in Time and Frequency Domain (1)

Lecture 3  Impulse and Frequency Response Functions

Lecture 4  Perturbation Signal Design

Lecture 5  Open and Closed Loop Systems, SISO and MIMO Systems

Lecture 6  Time Domain Models

Lecture 7  Correlation Functions in Time and Frequency Domain (2)

Lecture 8  Optimization Methods

Lecture 9  Physical Modeling, Model and Parameter Accuracy

Lecture 10  Non Linear Models

Lecture 11  Identification of Joint Impedance