Rights statement: This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 93, 2019 DOI: 10.1016/j.conengprac.2019.104165
Accepted author manuscript, 1.41 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Identification of continuous-time models with slowly time-varying parameters
AU - Padilla, A.
AU - Garnier, H.
AU - Young, P.C.
AU - Chen, F.
AU - Yuz, J.I.
N1 - This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 93, 2019 DOI: 10.1016/j.conengprac.2019.104165
PY - 2019/12/1
Y1 - 2019/12/1
N2 - The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.
AB - The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.
KW - Continuous-time model identification
KW - Instrumental variable method
KW - Linear filter methods
KW - Linear time-varying system
KW - Recursive methods
KW - Continuous time systems
KW - Intelligent systems
KW - Monte Carlo methods
KW - Numerical methods
KW - Time varying control systems
KW - Continuous time modeling
KW - Instrumental variable methods
KW - Linear filter method
KW - Linear time-varying systems
KW - Parameter estimation
U2 - 10.1016/j.conengprac.2019.104165
DO - 10.1016/j.conengprac.2019.104165
M3 - Journal article
VL - 93
JO - Control Engineering Practice
JF - Control Engineering Practice
SN - 0967-0661
M1 - 104165
ER -