Discrete-to-continuous dynamics reconstruction for bilinear systems

P. Rumschinski; Dina Shona Laila; R. Findeisen

doi:10.1109/CDC.2012.6426859

Discrete-to-continuous dynamics reconstruction for bilinear systems

P. Rumschinski, Dina Shona Laila, R. Findeisen

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

In this work, we study the reconstruction of continuous-time models from discrete-time models of bilinear systems. Many dynamical systems contain nonlinearities and evolve continuously in time. However, due to the use of digital sensors for data acquisition, system identification methods typically rely on sampled data and yield a discrete-time model. Linking such a discrete-time model to its equivalent continuous-time dynamics is nontrivial. This paper proposes a discrete-to-continuous dynamics reconstruction for discrete-time models of a class of bilinear systems obtained by system identification. We show that for bilinear systems we can obtain a discrete-time model that is consistent with the measurement data. Furthermore, we show that one can derive from the discrete-time model, its equivalent continuous-time model. Two examples illustrate the approach.

Original language	English
Title of host publication	2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
Publisher	IEEE
Pages	172-177
ISBN (Electronic)	978-1-4673-2066-5
ISBN (Print)	978-1-4673-2065-8
DOIs	https://doi.org/10.1109/CDC.2012.6426859
Publication status	Published - 4 Feb 2013
Event	IEEE Conference on Decision and Control - Hawaii, Maui, United States Duration: 10 Dec 2012 → 13 Dec 2012

Conference

Conference	IEEE Conference on Decision and Control
Country/Territory	United States
City	Maui
Period	10/12/12 → 13/12/12

Bibliographical note

The full text is currently unavailable on the repository.

Keywords

Nonlinear systems
Parameter estimation
Data models
Linear systems
Numerical models
Vectors
Time measurement
sampled data systems
bilinear systems
continuous time systems
data acquisition
discrete time systems
identification
equivalent continuous-time model
dynamical systems
nonlinearities
digital sensors
system identification methods
sampled data
discrete-time models
discrete-to-continuous dynamics reconstruction
measurement data
Bilinear system
Nonlinear identification
Discrete-to-continuous reconstruction

Access to Document

10.1109/CDC.2012.6426859

Cite this

@inproceedings{bac2984d4fa44b8bae73a2b878e27cdd,

title = "Discrete-to-continuous dynamics reconstruction for bilinear systems",

abstract = "In this work, we study the reconstruction of continuous-time models from discrete-time models of bilinear systems. Many dynamical systems contain nonlinearities and evolve continuously in time. However, due to the use of digital sensors for data acquisition, system identification methods typically rely on sampled data and yield a discrete-time model. Linking such a discrete-time model to its equivalent continuous-time dynamics is nontrivial. This paper proposes a discrete-to-continuous dynamics reconstruction for discrete-time models of a class of bilinear systems obtained by system identification. We show that for bilinear systems we can obtain a discrete-time model that is consistent with the measurement data. Furthermore, we show that one can derive from the discrete-time model, its equivalent continuous-time model. Two examples illustrate the approach.",

keywords = "Nonlinear systems, Parameter estimation, Data models, Linear systems, Numerical models, Vectors, Time measurement, sampled data systems, bilinear systems, continuous time systems, data acquisition, discrete time systems, identification, equivalent continuous-time model, dynamical systems, nonlinearities, digital sensors, system identification methods, sampled data, discrete-time models, discrete-to-continuous dynamics reconstruction, measurement data, Bilinear system, Nonlinear identification, Discrete-to-continuous reconstruction",

author = "P. Rumschinski and Laila, {Dina Shona} and R. Findeisen",

note = "The full text is currently unavailable on the repository.; IEEE Conference on Decision and Control ; Conference date: 10-12-2012 Through 13-12-2012",

year = "2013",

month = feb,

day = "4",

doi = "10.1109/CDC.2012.6426859",

language = "English",

isbn = "978-1-4673-2065-8",

pages = "172--177",

booktitle = "2012 IEEE 51st IEEE Conference on Decision and Control (CDC)",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Discrete-to-continuous dynamics reconstruction for bilinear systems

AU - Rumschinski, P.

AU - Laila, Dina Shona

AU - Findeisen, R.

N1 - The full text is currently unavailable on the repository.

PY - 2013/2/4

Y1 - 2013/2/4

N2 - In this work, we study the reconstruction of continuous-time models from discrete-time models of bilinear systems. Many dynamical systems contain nonlinearities and evolve continuously in time. However, due to the use of digital sensors for data acquisition, system identification methods typically rely on sampled data and yield a discrete-time model. Linking such a discrete-time model to its equivalent continuous-time dynamics is nontrivial. This paper proposes a discrete-to-continuous dynamics reconstruction for discrete-time models of a class of bilinear systems obtained by system identification. We show that for bilinear systems we can obtain a discrete-time model that is consistent with the measurement data. Furthermore, we show that one can derive from the discrete-time model, its equivalent continuous-time model. Two examples illustrate the approach.

AB - In this work, we study the reconstruction of continuous-time models from discrete-time models of bilinear systems. Many dynamical systems contain nonlinearities and evolve continuously in time. However, due to the use of digital sensors for data acquisition, system identification methods typically rely on sampled data and yield a discrete-time model. Linking such a discrete-time model to its equivalent continuous-time dynamics is nontrivial. This paper proposes a discrete-to-continuous dynamics reconstruction for discrete-time models of a class of bilinear systems obtained by system identification. We show that for bilinear systems we can obtain a discrete-time model that is consistent with the measurement data. Furthermore, we show that one can derive from the discrete-time model, its equivalent continuous-time model. Two examples illustrate the approach.

KW - Nonlinear systems

KW - Parameter estimation

KW - Data models

KW - Linear systems

KW - Numerical models

KW - Vectors

KW - Time measurement

KW - sampled data systems

KW - bilinear systems

KW - continuous time systems

KW - data acquisition

KW - discrete time systems

KW - identification

KW - equivalent continuous-time model

KW - dynamical systems

KW - nonlinearities

KW - digital sensors

KW - system identification methods

KW - sampled data

KW - discrete-time models

KW - discrete-to-continuous dynamics reconstruction

KW - measurement data

KW - Bilinear system

KW - Nonlinear identification

KW - Discrete-to-continuous reconstruction

U2 - 10.1109/CDC.2012.6426859

DO - 10.1109/CDC.2012.6426859

M3 - Conference proceeding

SN - 978-1-4673-2065-8

SP - 172

EP - 177

BT - 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

PB - IEEE

T2 - IEEE Conference on Decision and Control

Y2 - 10 December 2012 through 13 December 2012

ER -

Discrete-to-continuous dynamics reconstruction for bilinear systems

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this