A novel strategy for identification and control of a class of linear systems

Subramanian, Krishna; Chandar, T. S.

doi:10.1080/00207721.2022.2076172

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…Up to now, the identification methods of linear system have a long history in industrial applications. [11][12][13][14] The finite impulse response (FIR) model is a typical model in linear systems. 15,16 At present, many parameter estimation methods have been proposed for FIR models.…”

Section: Introductionmentioning

confidence: 99%

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A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

Zheng,

Ding

2024

Intl J Robust & Nonlinear

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This article considers the parameter identification problems of stochastic systems which described by the finite impulse response moving average model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The data filtering technique is to use a filter to filter the input and output data of the system disturbed by colored noise so as to improve the identification accuracy. By using the data filtering technique, this article proposes a filtering‐based recursive extended least squares (F‐RELS) algorithm. The convergence analysis indicates that the parameter estimates can converge to their true values. Compared with the recursive extended least squares algorithm, the proposed F‐RELS algorithm can obtain more accurate parameter estimation. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

“…Only the structures and parameters of the mathematical models should be known in prior can we design robust controllers for those models. Up to now, the identification methods of linear system have a long history in industrial applications 11–14 . The finite impulse response (FIR) model is a typical model in linear systems 15,16 .…”

Section: Introductionmentioning

confidence: 99%

A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

Zheng,

Ding

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

A novel strategy for identification and control of a class of linear systems

Cited by 1 publication

References 28 publications

A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

A filtering‐based recursive extended least squares algorithm and its convergence for finite impulse response moving average systems

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