Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912085824241664 |
|---|---|
| author | González, Rodrigo A. van Haren, Max Oomen, Tom Rojas, Cristian R. |
| author_facet | González, Rodrigo A. van Haren, Max Oomen, Tom Rojas, Cristian R. |
| contents | The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect signal reconstructability, careful consideration is required when identifying parametric and nonparametric models. In this letter, a comprehensive statistical analysis of estimators under slow sampling is performed. Necessary and sufficient conditions are obtained for unbiased estimates of the frequency response function beyond the Nyquist frequency, and it is shown that consistency of parametric estimators can be achieved even if input frequencies overlap after aliasing. Monte Carlo simulations confirm the theoretical properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency González, Rodrigo A. van Haren, Max Oomen, Tom Rojas, Cristian R. Systems and Control The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect signal reconstructability, careful consideration is required when identifying parametric and nonparametric models. In this letter, a comprehensive statistical analysis of estimators under slow sampling is performed. Necessary and sufficient conditions are obtained for unbiased estimates of the frequency response function beyond the Nyquist frequency, and it is shown that consistency of parametric estimators can be achieved even if input frequencies overlap after aliasing. Monte Carlo simulations confirm the theoretical properties. |
| title | Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.19629 |