An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability

Tong, Yuze; Zheng, Zongsheng; Wang, Fan; Li, Quanyou; Liu, Zhigang

doi:10.1016/j.dsp.2021.103324

Cited by 9 publications

(13 citation statements)

References 30 publications

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“…Table 1 provides the statistical results of the mean relative error (MRE), maximum relative error (MAXRE), mean absolute error (MAE), and root mean square error (RMSE) for the four reactors. The error formulas are shown in Equations ( 26)- (28). The results demonstrate that χ ¼ χ 1 , χ 2 , χ 3 , χ 4 ½ estimated by the augmented state equation and UKF algorithm can be utilized in the established SCSTR model to predict the outlet ion concentrations of the reactors in the cobalt removal process.…”

Section: Analysis Of Parameter Convergence and Model Reliabilitymentioning

confidence: 99%

“…The UKF algorithm not only generalizes the Kalman filter to the non-linear field, but also does not require linearization of the non-linear system, omitting the process of solving the Jacobian matrix and thereby avoiding the model error introduced by linearization. [28] The unknown parameters χ i in Equation ( 6) are characterized as state variables, so that the augmented state space includes the original state space and model parameters and the augmented state equation can be established. The augmented state equation and UKF algorithm constitute the model parameter estimation algorithm of the cobalt removal process.…”

Section: Parameter Estimation Based On the Augmented State Equation A...mentioning

confidence: 99%

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Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

Wang

Tang

et al. 2023

Can J Chem Eng

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The cobalt removal process with arsenic salt of zinc hydrometallurgy has serious non‐linearity, uncertainty, and mutual coupling. Its accurate dynamic modelling has always been a challenging problem. On the basis of in‐depth analysis of cobalt removal process and reaction mechanism, considering the cascade relationship between the reactors, a dynamic synergistic continuously stirred tank reactor (SCSTR) mechanism model of the cobalt removal process was constructed. Aiming at the unknown parameters in the SCSTR model, the idea of the Kalman filter was introduced, and the unknown parameters were characterized as unknown states; a method of estimating the unknown model parameters was developed using the augmented state equation and the unscented Kalman filter (UKF) algorithm. Simulation results with industrial data of a zinc smeltery showed that the parameter estimation model has high accuracy, and the estimated parameters can be used in the SCSTR model. An intensive simulation analysis of the dynamic characteristics of the complete SCSTR model was carried out to verify the influence of different input disturbances on the output ion concentration of each reactor, which demonstrated the excellent dynamic performance and potential of the model. Ultimately, according to the industrial calculation analysis, the SCSTR model has a guiding effect on the addition of zinc powder in the reactors, overcomes the blindness in the production process, and provides a momentous basis for the optimization control of the cobalt removal process.

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Section: Analysis Of Parameter Convergence and Model Reliabilitymentioning

confidence: 99%

Section: Parameter Estimation Based On the Augmented State Equation A...mentioning

confidence: 99%

Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

Wang

Tang

et al. 2023

Can J Chem Eng

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“…Please refer to [26] for formulating the error dynamics in detail. Recalling Remark 5, the error dynamics (20) should satisfy the conditions presented in Equation (15) for the EKFJDM to be exponentially bounded in the mean square. In this context, we introduce necessary bounds that need to satisfy for proving the stochastic stability of EKFJDM [27].…”

Section: Statementmentioning

confidence: 99%

“…The literature witnesses various developments [11,[14][15][16][17][18][19][20] to handle the delayed measurements independently (considering that no measurement is missing). More specifically, [11] and [15] handle small delays (up to two sampling intervals).…”

Section: Introductionmentioning

confidence: 99%

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Improved Gaussian filtering for handling concurrent delayed and missing measurements

Kumar

Naik

Pachori

et al. 2023

Asian Journal of Control

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This article addresses the Gaussian filtering problem under the environment of jointly occurring delayed and missing measurements. In this work, the former irregularity is incorporated (in the measurement model) using a Bernoulli random variable (BRV) and a geometric random variable, while the latter is subsumed with the help of the BRV; thereby, it enables to take account of large delay extents efficiently. Specifically, a modified measurement model, which incorporates the concerned irregularities, is introduced. Accordingly, the measurement‐related statistical parameters, that is, measurement estimate, covariance, and cross‐covariance, are rederived with respect to the modified measurement model. The rederived parameters replace the corresponding ones in the traditional Gaussian filtering algorithm, resulting in the proposed Gaussian filter. The simulation results conclude the superior performance of the proposed filter over the existing filters in handling the coexisting delay and missing measurement irregularities.

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Improved unscented Kalman filter for state of charge estimation of lithium-ion battery with one-step randomly measurement loss and inaccurate noise covariance matrices

Tong

Zheng²,

Wang³

et al. 2022

Digital Signal Processing

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An improved unscented Kalman filter for nonlinear systems with one-step randomly delayed measurement and unknown latency probability

Cited by 9 publications

References 30 publications

Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

Improved Gaussian filtering for handling concurrent delayed and missing measurements

Improved unscented Kalman filter for state of charge estimation of lithium-ion battery with one-step randomly measurement loss and inaccurate noise covariance matrices

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