Non-asymptotic numerical differentiation: a kernel-based approach
| Main Author: | Li, Peng; Pin, Gilberto; Fedele, Giuseppe; Parisini, Thomas |
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| Format: | Article Journal |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1489691 |
Daftar Isi:
- The derivative estimation problem is addressed in this paper by using Volterra integral operators which allow to obtain the estimates of the time derivatives with fast convergence rate. A deadbeat state observer is used to provide the estimates of the derivatives with a given fixed-time convergence. The estimation bias caused by modelling error is characterised herein as well as the ISS property of the estimation error with respect to the measurement perturbation. A number of numerical examples are carried out to show the effectiveness of the proposed differentiator also including comparisons with some existing methods.
- 2018 Taylor & Francis Copyright. The final publication is available at www.tandfonline.com via https://doi.org/10.1080/00207179.2018.1478130. P. Li, G. Pin, G. Fedele, and Thomas Parisini, Non-asymptotic numerical differentiation: a kernel-based approach, International Journal of Control, vol. 91, no. 9, pp. 2090-2099, 2018.