Robust Control for Dynamic Train Regulation in Fully Automatic Operation System Under Uncertain Wireless Transmissions

Wang, Xi; Su, Shuai; Cao, Yuan; Wang, Xiaoliang

doi:10.1109/tits.2022.3170950

Cited by 54 publications

(23 citation statements)

References 37 publications

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“…The proposed F-AM-RGEG algorithm in this paper can combine some statistical tools and optimal strategies [74][75][76][77][78][79][80] to study the parameter estimation algorithms of various stochastic systems with disturbances [81][82][83][84][85] and can be applied to literatures [86][87][88][89][90] such as paper-making systems and information processing systems and so on. The flowchart of computing the parameter estimation vector θ(t) by using the F-AM-RGEG algorithm in ( 73)-( 87) is shown in Figure 1 and the procedures are as follows.…”

Section: Filtered Auxiliary Model Recursive Generalized Extended Grad...mentioning

confidence: 99%

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Ding

Xiao

et al. 2023

Intl J Robust & Nonlinear

120

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For equation-error autoregressive moving average systems, that is, Box-Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi-innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi-innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi-innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises.

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Section: Filtered Auxiliary Model Recursive Generalized Extended Grad...mentioning

confidence: 99%

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Ding

Xiao

et al. 2023

Intl J Robust & Nonlinear

120

View full text Add to dashboard Cite

show abstract

“…Obviously, the MI-GI algorithm is also suitable for online identification and can improve algorithm performance by taking full advantage of all the collected data. The methods proposed in this paper can combine some statistical tools and optimal strategies [60][61][62][63][64][65] to study the parameter estimation algorithms of linear and nonlinear systems [66][67][68][69][70][71][72] and can be applied to other fields [73][74][75][76][77][78] such as paper-making systems, information processing, engineering systems and so on.…”

Section: The Multi-innovation Gradient Based Iterative Algorithmmentioning

confidence: 99%

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Sun

Ding

et al. 2023

Adaptive Control & Signal

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This paper focuses on the parameter estimation issues of multivariate equation-error autoregressive moving average systems. By applying the gradient search and the multi-innovation theory, we derive a multi-innovation gradient based iterative (MI-GI) algorithm. In order to improve the computational efficiency and the parameter estimation accuracy, a filtering and decomposition based gradient iterative (F-D-GI) algorithm is presented by using the data filtering technique and the decomposition technique. The key is to choose an appropriate filter to filter the input-output data and to transform an original system into several subsystems. Compared with the MI-GI algorithm, the F-D-GI algorithm can generate more accurate parameter estimates. Finally, an illustrative example is provided to indicate the effectiveness of the proposed algorithms.

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“…Therefore,

\hat{\boldsymbol{\varTheta}}\left({r}_{q-1}\right)

in Equations () and () are modified into

{\hat{\boldsymbol{\varTheta}}}_c\left({r}_{q-1}\right)

. The methods proposed in this article can combine some optimal strategies 58‐64 to study the parameter estimation algorithms of linear and nonlinear systems 65‐69 and can be applied to other fields 70‐75 such as paper‐making systems, information processing, engineering systems and so on.…”

Section: The Auxiliary Model‐based Bias Compensation Multi‐innovation...mentioning

confidence: 99%

“…Therefore, Θ(r q−1 ) in Equations ( 43) and ( 54) are modified into Θc (r q−1 ). The methods proposed in this article can combine some optimal strategies [58][59][60][61][62][63][64] to study the parameter estimation algorithms of linear and nonlinear systems [65][66][67][68][69] and can be applied to other fields [70][71][72][73][74][75] such as paper-making systems, information processing, engineering systems and so on.…”

Section: Bias Compensation-based Identification Algorithmmentioning

confidence: 99%

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data

Liu

2023

Adaptive Control & Signal

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Summary Missing data often occur in industrial processes. In order to solve this problem, an auxiliary model and a particle filter are adopted to estimate the missing outputs, and two unbiased parameter estimation methods are developed for a class of nonlinear systems (e.g., bilinear systems) with irregularly missing data. Firstly, an auxiliary model is constructed to estimate the unknown output, and an auxiliary model‐based multi‐innovation recursive least squares algorithm is presented by expanding the scalar innovation to an innovation vector. Secondly, according to the bias compensation principle, an auxiliary model‐based bias compensation multi‐innovation recursive least squares algorithm is proposed to compensate the bias caused by the colored noise. Thirdly, for further improving the parameter estimation accuracy, the unknown true output is estimated by a particle filter, and a particle filtering‐based bias compensation multi‐innovation recursive least squares algorithm is developed. Finally, a numerical example is selected to validate the effectiveness of the proposed algorithms. The simulation results indicate that the proposed algorithms have good performance in identifying bilinear systems with irregularly missing data.

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Robust Control for Dynamic Train Regulation in Fully Automatic Operation System Under Uncertain Wireless Transmissions

Cited by 54 publications

References 37 publications

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Unbiased recursive least squares identification methods for a class of nonlinear systems with irregularly missing data

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