Bias-compensating least squares method for identification of continuous-time systems from sampled data

This paper proposes a bias-compensated least squares (BCLS) method for a continuous-time model estimation in closed loop environment. The feedback controller is assumed to be linear time invariant discrete-time system and the feedback system is assumed to be asymptotically stable. It is also assumed that the control input is a zeroth order hold signal. The aim of the proposed method is to reduce the computational cost which will be required for the conventional method.

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“…Based on a similar idea of [6], [11], the noise variance σ 2 e is to be estimated from the a posteriori error or the least squares residual.…”

Section: Estimation Of the Noise Variancementioning

confidence: 99%

“…The authors have applied the bias compensated least squares (BCLS) method [6], [11] to the closed loop identification [3]. The method proposed in [3] is that the transfer function from white noise to the output, say W ye (q), is estimated once at the first step and the input and output of the plant is filtered by using W −1 ye (q).…”

Section: Introductionmentioning

confidence: 99%

BCLS method for the estimation of continuous-time models in closed loop enviornment

Ikeda

Mogami

Shimomura

2010

2010 IEEE International Conference on Control Applications

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“…have been developed lately. Among them, noteworthy are those based on the introduction of a forgetting factor in recursively least squares or Kalman ÿlter based algorithms [7,[13][14][15]. In these papers, it was pointed out that issues related to the 'divergence' of the estimated parameters should be considered and thus appropriate values of the forgetting factor, either constant (optimal) or variable, were proposed.…”

Section: Introductionmentioning

confidence: 99%

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Lin

Betti

2003

Earthq Engng Struct Dyn

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SUMMARYThis paper presents an adaptive on-line identiÿcation algorithm based on a newly deÿned variable forgetting factor approach. At each time step, the proposed methodology, a recursive least-square (Kalman ÿlter) based algorithm, upgrades the adaptation gain matrix using an adaptive forgetting factor that is expressed as the ratio between the minimum value of the diagonal elements of the adaptation gain matrix and a set of pre-deÿned threshold values. This approach requires only acceleration measurements and is particularly robust to the integration errors introduced in determining velocities and displacements. Such an algorithm has been implemented for both parametric and non-parametric identiÿcation studies. For the case of no a priori information on the type of the structural model, an analytical method based on a power series of multivariable polynomial expansions has been introduced. The e ectiveness and robustness of the proposed approach has been shown in various numerical simulations, considering the e ects of excitation amplitude, of measurement noise, of a priori mass estimation error, and of exact-, under-, and over-parameterization of the structural model. General polynomial-type non-linear models such as Du ng and Van der Pole oscillators and Bouc-Wen hysteretic model have been analyzed using the proposed modeling process. The methodology presented in this paper leads to a more accurate and controllable on-line identiÿcation either for the estimation of time-invariant or time-varying structural parameters (for damage detection purposes) or for the prediction of the future structural response to dynamic loading.

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“…Unbiased estimate is also obtained by using the bias compensated least squares (BCLS) method [8], [9], which is based on the analysis of the noise effect on the LS estimate and on the estimation of the noise variance. Therefore, BCLS will be applicable for the direct approach of the closed-loop identification.…”

Section: Introductionmentioning

confidence: 99%

Bias Compensation of Recursive Least Squares Estimate in Closed Loop Environment

Ikeda

Mogami

Shimomura

2008

SICE Journal of Control, Measurement, and System Integration

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In this paper, an asymptotic bias of the recursive least squares (RLS) estimate in the closed loop environment is analyzed and its compensation method is proposed under the assumption that the noise is white. Namely, a bias compensated RLS method in the closed loop environment based on output error (OE) model is proposed. A posteriori error is also analyzed for the estimation of the noise variance.

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Bias-compensating least squares method for identification of continuous-time systems from sampled data

Cited by 29 publications

References 11 publications

BCLS method for the estimation of continuous-time models in closed loop enviornment

BCLS method for the estimation of continuous-time models in closed loop enviornment

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Bias Compensation of Recursive Least Squares Estimate in Closed Loop Environment

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