Open- and Closed Loop Systems & Multivariable Systems
This lecture will first explain the answers of Assignment 1 and then sources for error in estimators and ways to improve the estimation will be discussed.
In most cases system identification is a battle against noise. One should try to decrease the power of the noise and/or increase the power of the signal. Several methods exist to boost the power of the signal and such improve the signal-to-noise ratio (SNR). However random signals always introduce leakage, an effect of the observation time and resulting discrete frequency resolution. Multisine signals are composed of multiple sines. These deterministic signal do not introduce leakage and as the power is distribute over a limited number of frequencies the power per frequency can be high. With cresting, a technique to minimize the ratio between the outliers of the time and the standard deviation of the signal, the power can even be further increased. And the effect of the input signal on the system identification procedure is discussed.
Book: Pintelon & Schoukens
- Chapter 2: all
- Chapter 4: all
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 https://ocw.tudelft.nl/courses/system-identification-and-parameter-estimation/.