The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Juan Yuz - One of the best experts on this subject based on the ideXlab platform.
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Identification of continuous-time models with slowly time-varying parameters
Control Engineering Practice, 2019Co-Authors: Arturo Padilla, Hugues Garnier, Peter Young, Fengwei Chen, Juan YuzAbstract: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.
Arturo Padilla - One of the best experts on this subject based on the ideXlab platform.
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Identification of continuous-time models with slowly time-varying parameters
Control Engineering Practice, 2019Co-Authors: Arturo Padilla, Hugues Garnier, Peter Young, Fengwei Chen, Juan YuzAbstract: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.
Fengwei Chen - One of the best experts on this subject based on the ideXlab platform.
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Identification of continuous-time models with slowly time-varying parameters
Control Engineering Practice, 2019Co-Authors: Arturo Padilla, Hugues Garnier, Peter Young, Fengwei Chen, Juan YuzAbstract: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.
Peter Young - One of the best experts on this subject based on the ideXlab platform.
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Identification of continuous-time models with slowly time-varying parameters
Control Engineering Practice, 2019Co-Authors: Arturo Padilla, Hugues Garnier, Peter Young, Fengwei Chen, Juan YuzAbstract: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.
Hugues Garnier - One of the best experts on this subject based on the ideXlab platform.
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Identification of continuous-time models with slowly time-varying parameters
Control Engineering Practice, 2019Co-Authors: Arturo Padilla, Hugues Garnier, Peter Young, Fengwei Chen, Juan YuzAbstract: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.