Variational Bayesian approach for ARX systems with missing observations and varying time-delays

Chen, Jing; Huang, Biao; Dai, Feng; Gu, Yu

doi:10.1016/j.automatica.2018.04.003

Cited by 77 publications

(30 citation statements)

References 28 publications

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“…[17][18][19] For example, in communication, the measurements are often obtained with time delay because of the transmission congestion; the communication networks between subsystems are often unreliable, which will introduce the communication delays. 20 Such delays may cause instability and poor performance of system dynamics. 20 Such delays may cause instability and poor performance of system dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Highly computationally efficient state filter based on the delta operator

Xiao

Ding

et al. 2019

Adaptive Control & Signal

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160

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The Kalman filter is not suitable for the state estimation of linear systems with multistate delays, and the extended state vector Kalman filtering algorithm results in heavy computational burden because of the large dimension of the state estimation covariance matrix. Thus, in this paper, we develop a novel state estimation algorithm for enhancing the computational efficiency based on the delta operator. The computation analysis and the simulation example show the performance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Some important variables of chemical processes are often obtained through online analyzers, resulting to large time delays. 20 Such delays may cause instability and poor performance of system dynamics. 21 Thus, the analysis and control of time-delay systems are important.…”

Section: Introductionmentioning

confidence: 99%

Highly computationally efficient state filter based on the delta operator

Xiao

Ding

et al. 2019

Adaptive Control & Signal

Self Cite

160

View full text Add to dashboard Cite

show abstract

“…26,27 However, the process noise should not be always ignored since it widely exists in physical processes due to environmental disturbance and modeling inaccuracies. 28,29 The process noise has intrinsic coupling with system dynamics and its statistical property is hard to determined. Feng et al proposed a recursive covariance estimation algorithm to obtain the optimal state estimates for linear systems with measurement and process noise, and it is proved to be consistent with ideal Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Wang

Zhu

Ding

2020

Adaptive Control & Signal

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This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.

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“…Existing subspace identification techniques handle missing data in a number of different ways. In addition to data imputation listed above other approaches carry out the prediction minimization algorithms using only available measurements 25,26 . Other approaches such as subspace clustering are more computationally complex than the proposed approach and also do not readily allow for online applications 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Subspace based model identification for missing data

2020

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This article addresses the problem of missing process data in data‐driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identification. Note that the existing subspace identification approaches often utilize singular value decomposition (SVD) as part of the identification algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identification matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The efficacy of the proposed approach is shown via simulation of a nonlinear batch process example.

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Variational Bayesian approach for ARX systems with missing observations and varying time-delays

Cited by 77 publications

References 28 publications

Highly computationally efficient state filter based on the delta operator

Highly computationally efficient state filter based on the delta operator

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Subspace based model identification for missing data

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