Indirect Continuous-Time System Identification - A Subspace Downsampling Approach

Jose A. Ramos; Paulo Lopes dos Santos; Teresa Paula Azevedo Perdicoulis; Gerhard Jank; Jorge L. Martins de Carvalho

doi:10.1109/cdc.2011.6160622

Indirect Continuous-Time System Identification - A Subspace Downsampling Approach

Jose A. Ramos
, Paulo Lopes dos Santos
, Teresa Paula Azevedo Perdicoulis
, Gerhard Jank
, Jorge L. Martins de Carvalho

Research output: Contribution to conference › Paper › peer-review

4 Scopus citations

Abstract

This article presents a new indirect identification method for continuous-time systems able to resolve the problem of fast sampling. To do this, a Subspace IDentification Down-Sampling (SIDDS) approach that takes into consideration the intermediate sampling instants of the input signal is proposed. This is done by partitioning the data set into m subsets, where m is the downsampling factor. Then, the discrete-time model is identified using a based subspace identification discrete-time algorithm where the data subsets are fused into a single one. Using the algebraic properties of the system, some of the parameters of the continuous-time model are directly estimated. A procedure that secures a prescribed number of zeros for the continuous-time model is used during the estimation process. The algorithm's performance is illustrated through an example of fast sampling, where its performance is compared with the direct methods implemented in Contsid.

Original language	American English
Pages	6463-6468
Number of pages	6
DOIs	https://doi.org/10.1109/cdc.2011.6160622
State	Published - Dec 1 2011
Event	2011 50th IEEE Conference on Decision and Control and European Control Conference - Orlando, United States Duration: Dec 12 2011 → Dec 15 2011 https://ieeexplore.ieee.org/xpl/conhome/6149620/proceeding

Conference

Conference	2011 50th IEEE Conference on Decision and Control and European Control Conference
Country/Territory	United States
City	Orlando
Period	12/12/11 → 12/15/11
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6149620/proceeding

ASJC Scopus Subject Areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Disciplines

Computer Sciences

Access to Document

10.1109/cdc.2011.6160622

Cite this

Ramos, J. A., Lopes dos Santos, P., Azevedo Perdicoulis, T. P., Jank, G., & Martins de Carvalho, J. L. (2011). Indirect Continuous-Time System Identification - A Subspace Downsampling Approach. 6463-6468. Paper presented at 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, Florida, United States. https://doi.org/10.1109/cdc.2011.6160622

Indirect Continuous-Time System Identification - A Subspace Downsampling Approach. / Ramos, Jose A.; Lopes dos Santos, Paulo; Azevedo Perdicoulis, Teresa Paula et al.
2011. 6463-6468 Paper presented at 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, Florida, United States.

Research output: Contribution to conference › Paper › peer-review

Ramos, JA, Lopes dos Santos, P, Azevedo Perdicoulis, TP, Jank, G & Martins de Carvalho, JL 2011, 'Indirect Continuous-Time System Identification - A Subspace Downsampling Approach', Paper presented at 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, United States, 12/12/11 - 12/15/11 pp. 6463-6468. https://doi.org/10.1109/cdc.2011.6160622

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abstract = " This article presents a new indirect identification method for continuous-time systems able to resolve the problem of fast sampling. To do this, a Subspace IDentification Down-Sampling (SIDDS) approach that takes into consideration the intermediate sampling instants of the input signal is proposed. This is done by partitioning the data set into m subsets, where m is the downsampling factor. Then, the discrete-time model is identified using a based subspace identification discrete-time algorithm where the data subsets are fused into a single one. Using the algebraic properties of the system, some of the parameters of the continuous-time model are directly estimated. A procedure that secures a prescribed number of zeros for the continuous-time model is used during the estimation process. The algorithm's performance is illustrated through an example of fast sampling, where its performance is compared with the direct methods implemented in Contsid.",

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Indirect Continuous-Time System Identification - A Subspace Downsampling Approach

Abstract

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