Optimal Iv Identification and Estimation of Continuous-Time Tf Models

Young, Peter C.

doi:10.3182/20020721-6-es-1901.01004

Cited by 31 publications

(20 citation statements)

References 14 publications

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“…We consider here one of the approaches commonly used in continuous‐time identification literature, namely the instrumental variable method. This approach has been extensively used for system identification …”

Section: Solution Of Estimation Equationsmentioning

confidence: 99%

“…The instrumental variable estimate of the parameters is given by:

θ^{IV} = false(Ψ^{T} Φ {false)}^{- 1} Ψ^{T} Γ

where

Ψ

is the instrument matrix that can be generated in different ways. Young used the predicted output and by replacing the output variable in the regressor matrix (

Φ

) with their predicted values, the instrument matrix was generated. This approach has been adopted in a series of other methods .…”

Section: Solution Of Estimation Equationsmentioning

confidence: 99%

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Identification from step response – The integral equation approach

Ahmed

2016

Can J Chem Eng

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An overview of identification of continuous‐time models from step responses using the integral equation approach is presented. Both open loop and closed loop identification as well as identification of multiple‐input‐multiple‐output (MIMO) models are considered. Solutions to practical implementation problems are provided and methods for identification with transient initial conditions using raw data as well as estimation algorithms in the presence of disturbances are outlined. The methodologies are presented in a simplified way using the example of a first order model; however, the algorithms are applicable for models with higher orders. Solution techniques for the estimation equations are also discussed. Identification results under different experimental conditions and data quality are presented to demonstrate the performance of the algorithms. A number of experimental and simulation examples are presented to demonstrate the applicability of the approach.

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Section: Solution Of Estimation Equationsmentioning

confidence: 99%

“…The instrumental variable estimate of the parameters is given by:

θ^{IV} = false(Ψ^{T} Φ {false)}^{- 1} Ψ^{T} Γ

where

Ψ

is the instrument matrix that can be generated in different ways. Young used the predicted output and by replacing the output variable in the regressor matrix (

Φ

) with their predicted values, the instrument matrix was generated. This approach has been adopted in a series of other methods .…”

Section: Solution Of Estimation Equationsmentioning

confidence: 99%

Identification from step response – The integral equation approach

Ahmed

2016

Can J Chem Eng

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“…In this iterative algorithm, the selection of the initialÂ 0 (s) polynomial does not have to be particularly accurate provided the prefilter based on it does not seriously attentuate any signals within the pass-band of the system being modelled (Young, 2002b). It is also assumed that studies based on model identification criteria have defined the orders n and m of the polynomials that characterize the TF model, as well as any pure time delays, the effects of which are removed prior to estimation.…”

Section: The Srivc Methodsmentioning

confidence: 99%

An Optimal Instrumental Variable Approach for Identifying Hybrid Continuous-Time Box-Jenkins Models

Young

Garnier

Gilson

2006

IFAC Proceedings Volumes

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The paper describes and evaluates an optimal instrumental variable method for identifying hybrid continuous-time transfer function models of the Box-Jenkins form from discrete-time sampled data, where the relationship between the measured input and output is a continuous-time transfer function, while the noise is represented as a discrete-time AR or ARMA process. The performance of the proposed hybrid parameter estimation scheme is evaluated by Monte Carlo simulation analysis.

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“…From the timedomain model-based point of view, because the time-delay exhibits inside the time index of state/input/output variables, some mathematical operation is often needed so as to be able to have the explicit expression of this parameter out of the time index, before any identification algorithm proceeds further. This mathematical operation can be realized through applying an integrator or derivative filter on both side of the system equation (Ahmed et al (2006);Young (2002)). These methods are only suitable for timeinvariant parameters and off-line identification, i.e., the input signal should be available beforehand.…”

Section: On-line Miso Tv-fopdt Identificationmentioning

confidence: 99%

Time-Varying FOPDT Modeling and On-line Parameter Identification

Yang

Sun

2013

IFAC Proceedings Volumes

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Abstract:A type of Time-Varying First-Order Plus Dead-Time (TV-FOPDT) model is extended from SISO format into a MISO version by explicitly taking the disturbance input into consideration. Correspondingly, a set of on-line parameter identification algorithms oriented to MISO TV-FOPDT model are proposed based on the Mixed-Integer-Nonlinear Programming, Least-MeanSquare and sliding window techniques. The proposed approaches can simultaneously estimate the time-dependent system parameters, as well as the unknown disturbance input if it is the case, in an on-line manner. The proposed concepts and algorithms are firstly illustrated through a numerical example, and then applied to investigate transient superheat dynamic modeling in a supermarket refrigeration system.

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Optimal Iv Identification and Estimation of Continuous-Time Tf Models

Cited by 31 publications

References 14 publications

Identification from step response – The integral equation approach

Identification from step response – The integral equation approach

An Optimal Instrumental Variable Approach for Identifying Hybrid Continuous-Time Box-Jenkins Models

Time-Varying FOPDT Modeling and On-line Parameter Identification

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