Identification of dual‐rate sampled systems with time delay subject to load disturbance

Dong, Shijian; Liu, Tao; Wang, Qingguo

doi:10.1049/iet-cta.2016.0941

Cited by 23 publications

(7 citation statements)

References 33 publications

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“…, it can readily be shown that V andθ are bounded. It needs to be pointed out that δ in (14) can be any constant greater than zero, the stability of V can be guaranteed as long as σ > 0. But the value of σ is related to the estimation performance, which would be discussed in Remark 2.…”

Section: On-line Estimation Of Tv Parametersmentioning

confidence: 99%

Robust Adaptive Identification of Linear Time-Varying Systems Under Relaxed Excitation Conditions

Zeng

2020

IEEE Access

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An on-line modified least-squares identification algorithm is proposed for linear time-varying systems with bounded disturbances under relaxed excitation conditions. An extra term which enhances the tracking ability for time-varying parameters is added to the covariance's update law. An indicator of the regressor's excitation level based on the maximum eigenvalue of the covariance matrix is developed. By combining the maximum eigenvalue with its variation trend shown by the sensitivity of the maximum eigenvalue to change in the covariance matrix, a novel identification law, which is switched between a modified least-squares algorithm and a gradient algorithm based on fixed σ-modification, is proposed. The boundedness of the estimation error and the covariance matrix are guaranteed via Lyapunov stability theory. The superiority of the proposed method is verified by simulations. INDEX TERMS Least-squares identification algorithms, linear time-varying systems, relaxed excitation conditions, covariance matrix.

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Section: On-line Estimation Of Tv Parametersmentioning

confidence: 99%

Robust Adaptive Identification of Linear Time-Varying Systems Under Relaxed Excitation Conditions

Zeng

2020

IEEE Access

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“…In most cases, it is not easy to analyse the whole system and to construct the mathematical model based on the analytic approach. Therefore, system identification becomes the first choice when people model a system [10][11][12], and has a wide application in both linear [13] and nonlinear systems [14][15][16][17]. For the multiple-input and multiple-output Box-Jenkins model with disturbances, a two-stage identification method was developed by using the residual model of Kalman filter [18].…”

Section: Introductionmentioning

confidence: 99%

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Liu

Ding

et al. 2019

IET Control Theory & Applications

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System identification provides many convenient and useful methods for engineering modeling. This paper targets the parameter identification problems for multivariable equation-error autoregressive moving average systems. To reduce the influence of the colored noises on the parameter estimation, the data filtering technique is adopted to filter the input and output data, and to transform the original system into a filtered system with white noises. Then we decompose the filtered system into several subsystems and develop a filtering based partially-coupled generalized extended stochastic gradient algorithm via the coupling concept. In contrast to the multivariable generalized extended stochastic gradient algorithm, the proposed algorithm can give more accurate parameter estimates. Finally, the effectiveness of the proposed algorithm is well demonstrated by simulation examples.

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“…Combining the integral technique and relay feedback test, a robust frequency domain identification algorithm is devised for the transfer function model with non‐zero initial conditions and disturbance [30]. In discrete‐time domain, we use interactive estimation theory to derive a robust identification algorithm with two RLS methods and adaptive FFs for Hammerstein non‐linear or linear dual‐rate sampled system under load disturbance [31, 32]. On the basis of the adaptive variable FF matrix scheme, a robust method is presented for linear output error models with load disturbance [33].…”

Section: Introductionmentioning

confidence: 99%

Recursive identification for Wiener non‐linear systems with non‐stationary disturbances

Dong

Zhang

Chen

2019

IET Control Theory & Applications

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Identification of dual‐rate sampled systems with time delay subject to load disturbance

Cited by 23 publications

References 33 publications

Robust Adaptive Identification of Linear Time-Varying Systems Under Relaxed Excitation Conditions

Robust Adaptive Identification of Linear Time-Varying Systems Under Relaxed Excitation Conditions

Partially coupled gradient estimation algorithm for multivariable equation‐error autoregressive moving average systems using the data filtering technique

Recursive identification for Wiener non‐linear systems with non‐stationary disturbances

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