Name
Title | ||
|---|---|---|
Linear System Identification in a Nonlinear Setting - Nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation. |
Abstract | ||
|---|---|---|
Linear system identification [1]-[4] is a basic step in modern control design approaches. Starting from experimental data, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system. At the same time, the power spectrum of the unmodeled disturbances is identified to generate uncertainty bounds on the estimated model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MCS.2016.2535918 | IEEE Control Systems Magazine |
Keywords | Field | DocType |
Nonlinear distortion,Data models,Uncertainty,Nonlinear systems,Linear systems,Distortion measurement,Frequency measurement | Linear approximation,Data modeling,Nonlinear system,Linear system,Experimental data,Control theory,Nonparametric statistics,Control engineering,Spectral density,Nonlinear distortion,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 3 | 1066-033X |
Citations | PageRank | References |
4 | 0.44 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johan Schoukens | 1 | 376 | 58.12 |
| Mark Vaes | 2 | 4 | 0.78 |
| Rik Pintelon | 3 | 1011 | 163.45 |