Finite-time <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1732" altimg="si9.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> control for linear Itô stochastic Markovian jump systems with Brownian motion and Poisson jumps

Yan, Zhiguo; Zhong, Shiyu; He, Shuping

doi:10.1016/j.sysconle.2022.105285

Cited by 27 publications

(4 citation statements)

References 32 publications

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“…The F-MI-GGI identification algorithm is also called the filtered moving-data-window generalized gradient-based iterative (F-MDW-GGI) algorithm or the moving-data-window filtered generalized gradient-based iterative (MDW-F-GGI) algorithm. The filtered multi-innovation iterative parameter estimation algorithm in this work for equation-error autoregressive systems can be extended to equation-error autoregressive moving average systems (i.e., controlled autoregressive autoregressive moving average systems) and other linear and nonlinear stochastic systems with ARMA noise [88][89][90][91][92][93][94] and can be applied to other areas [95][96][97][98][99][100][101][102] such as paper-making and chemical engineering systems.…”

Section: The Filtered Multi-innovation Generalized Gradient-based Ite...mentioning

confidence: 99%

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea

Ding,

Shao,

et al. 2024

Adaptive Control & Signal

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SummaryBy using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi‐innovation generalized projection‐based iterative identification method, a filtered generalized gradient‐based iterative identification method, a filtered generalized least squares‐based iterative identification method, a filtered multi‐innovation generalized gradient‐based iterative identification method and a filtered multi‐innovation generalized least squares‐based iterative identification method for equation‐error autoregressive systems described by the equation‐error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises.

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Section: The Filtered Multi-innovation Generalized Gradient-based Ite...mentioning

confidence: 99%

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea

Ding,

Shao,

et al. 2024

Adaptive Control & Signal

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“…The proposed RELS parameter estimation algorithm in this article for finite impulse response systems can be extended to controlled autoregressive moving average systems and other linear and nonlinear stochastic systems with ARMA noise [93][94][95][96][97][98] and can be applied to other areas [99][100][101][102][103][104] such as industrial process systems. [105][106][107][108][109][110] The steps involved in the RELS algorithm for FIR-MA systems are listed in the following:…”

Section: V(t) ∶= Y(t) − φT (T) θ(T)mentioning

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

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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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“…The proposed parameter estimation algorithm in this article for CARMA systems can be extended to controlled autoregressive moving average systems and other linear and nonlinear stochastic systems with ARMA noises [97][98][99][100][101][102] and can be applied to other areas [103][104][105][106][107][108] such as industrial process systems. [109][110][111][112][113][114] The steps of computing the parameter estimation vectors â(t), b(t), and d(t) by the P-HREG algorithm in ( 53)-( 74) are listed as follows.…”

Section: The Hierarchical Recursive Extended Gradient Algorithm With ...mentioning

confidence: 99%

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

Sun,

Xiong,

Ding

et al. 2024

Intl J Robust & Nonlinear

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This article considers the parameter estimation problems for the controlled autoregressive systems interfered by moving average noises. A recursive extended gradient algorithm with penalty term is proposed by using the penalty criterion function. By introducing three fictitious output variables, the original system can be decomposed into three subsystems based on the hierarchical principle. The hierarchical recursive extended gradient algorithm with penalty term is then proposed to achieve the parameter estimation. Finally, the experimental results demonstrate the effectiveness of the proposed algorithms.

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Finite-time H2/H∞ control for linear Itô stochastic Markovian jump systems with Brownian motion and Poisson jumps

Cited by 27 publications

References 32 publications

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea

Filtered generalized iterative parameter identification for equation‐error autoregressive models based on the filtering identification idea

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

Hierarchical estimation methods based on the penalty term for controlled autoregressive systems with colored noises

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