A Case Study in Model Reduction of Linear Time-Varying Systems
(2004) p.249-254- Abstract
- In this paper the balanced truncation procedureis applied to a time-varying linear system, both in continuous and in discrete time. It is discussed how to obtain the reduced-order systems by using certainprojections instead of direct balancing. An approximative zero-order-hold discretization of continuous-time systems is described, and a new a priori approximation error bound for balanced truncation in the discrete-time case is obtained. The case study shows that there are several advantages to work in discrete time, including simpler implementation and less computations.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/536055
- author
- Sandberg, Henrik LU
- organization
- publishing date
- 2004
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 2nd IFAC Workshop on Periodic Control Systems
- pages
- 249 - 254
- language
- English
- LU publication?
- yes
- id
- 012fda2c-362f-4c66-8b5f-af103c012674 (old id 536055)
- date added to LUP
- 2016-04-04 14:25:56
- date last changed
- 2021-09-27 10:42:50
@inproceedings{012fda2c-362f-4c66-8b5f-af103c012674, abstract = {{In this paper the balanced truncation procedureis applied to a time-varying linear system, both in continuous and in discrete time. It is discussed how to obtain the reduced-order systems by using certainprojections instead of direct balancing. An approximative zero-order-hold discretization of continuous-time systems is described, and a new a priori approximation error bound for balanced truncation in the discrete-time case is obtained. The case study shows that there are several advantages to work in discrete time, including simpler implementation and less computations.}}, author = {{Sandberg, Henrik}}, booktitle = {{Proceedings of the 2nd IFAC Workshop on Periodic Control Systems}}, language = {{eng}}, pages = {{249--254}}, title = {{A Case Study in Model Reduction of Linear Time-Varying Systems}}, year = {{2004}}, }