Unscented Kalman filter with advanced adaptation of scaling parameter

Straka, Ondřej; Duník, Jindřich; Šimandl, Miroslav

doi:10.1016/j.automatica.2014.08.030

Cited by 45 publications

(52 citation statements)

References 24 publications

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“…There are many adaptive UKF methods that adaptively estimate the covariance of the system noise and observation noise . For some methods, the spread of the sigma points is adapted to cover the nonlinearity of the system . S. S. Bisht successfully used the UKF method to track sudden changes of the stiffness in the multiple degree of freedom structure .…”

Section: Introductionmentioning

confidence: 99%

“…[24,25] For some methods, the spread of the sigma points is adapted to cover the nonlinearity of the system. [26] S. S. Bisht successfully used the UKF method to track sudden changes of the stiffness in the multiple degree of freedom structure. [25] The UKF method was applied by M. S. Miah, et al to identify the states and parameters of linear quadratic regulator control, and this control scheme is supported by experimental validation.…”

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confidence: 99%

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An UKF-based nonlinear system identification method using interpolation models and backward integration

Qin

2018

Struct Control Health Monit

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SummaryIn this paper, a novel identification method for nonlinear systems is proposed.This method utilizes linear interpolation models to describe the nonlinear forces of the physical models, and the unscented Kalman filter (UKF) method is adopted for the task of nonlinear identification. With the help of a linear interpolation algorithm, the proposed method requires little prior knowledge of the form of the nonlinear stiffness. Therefore, this method takes advantage of both the independence of the linear interpolation points and the inherent mathematical properties of the UKF. The UKF method is also modified to better fit the needs of parameter identification. To further emphasize parameter identification, backward integration and observations of the previous states are used. Two numerical simulations of the nonlinear elastic stiffness and Bouc-Wen hysteresis are conducted to show the flexibility and efficiency of this method. In these 2 examples, the observation signals are generated by analytic models, and the identifications are conducted with a linear interpolation model. | INTRODUCTIONA suitable mathematical model for civil structures is very important for health monitoring and structural control. For structural health monitoring, if a simple and accurate model can be obtained based on the acquired data, then future structural behaviors can be predicted by this model, and some possible damages or failures may be avoided. [1][2][3][4] The effects of structural control depend on the structural model, even for intelligent control methods. [5][6][7][8] The more accurate a model that can be obtained, the better control effect can be realized. In a civil structural model, nonlinearity commonly exists with respect to health monitoring and structural control. Simply assuming linearity of the structure behavior will cause significant error. To describe a particular nonlinear behavior, there are usually plenty of proper mathematical models available. Examples include the polynomial model [9,10] and multilinear model [11] for elastic stiffness, BoucWen model, [12] Preisach model, [13] and Iwan model [14] for hysteretic force. Different models fit for different problems.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

An UKF-based nonlinear system identification method using interpolation models and backward integration

Qin

2018

Struct Control Health Monit

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“…Over the past decades, the filtering problem of nonlinear systems has been an active field of research on account of its widespread applications, for example, dynamic target tracking [1,2], signal processing [3], and integrated navigation [4]. As a general rule, in view of the fact that the Bayesian estimator of nonlinear systems in minimum mean square error (MMSE) sense is usually faced with intractable computation [5], consequently using approximation methods to design cost-efficient estimators has received much attention.…”

Section: Introductionmentioning

confidence: 99%

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Yang

Meng

et al. 2018

Mathematical Problems in Engineering

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This paper proposes a novel strong tracking filter (STF), which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the onestep random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF) based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF) algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP). Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.

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“…Based on the above criterion, an optimal SP minimising the normalised squared measurement prediction error can be obtained, and the quality of the UKF estimation can be improved accordingly (Straka et al, 2014). In this work, a cooperative evolutionary algorithm combining particle swarm optimisation with differential evolution is employed for the SP adaptation (Lei et al, 2013c).…”

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confidence: 99%

Navigation Performance of the Libration Point Satellite Navigation System for Future Mars Exploration

Zhang

2015

J. Navigation

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Based on the candidate architectures of the libration point satellite navigation system, a Mars navigation performance analysis is conducted in this paper to further verify the feasibility of the Universe Lighthouse. Firstly, a high-fidelity Mars exploration mission is developed as the reference scenario. Then, with the use of a novel adaptive unscented Kalman filter, navigation performance of the candidate Earth-Moon L 1,2,4,5 four-satellite constellations is evaluated by Monte-Carlo simulations. The final results indicate that the libration point satellite navigation system is available for Mars navigation and the effects of different constellation configurations and measurement types are also compared and analysed. K E Y WO R D S 1. Libration Point Satellite.2. Mars Navigation. 3. Numerical Simulation.

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Unscented Kalman filter with advanced adaptation of scaling parameter

Cited by 45 publications

References 24 publications

An UKF-based nonlinear system identification method using interpolation models and backward integration

An UKF-based nonlinear system identification method using interpolation models and backward integration

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Navigation Performance of the Libration Point Satellite Navigation System for Future Mars Exploration

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