Distributed Estimation Fusion With Application to a Multisensory Vehicle Suspension System With Time Delays

Lee, Seokhyoung; Jeon, Moongu; Shin, Vladimir

doi:10.1109/tie.2011.2182010

Cited by 12 publications

(15 citation statements)

References 23 publications

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“…The similar time-delay models can be also found in [34] and [4]. Different from these available references, in which the introduced time delays are constructed either by the linearly weighted sum of the delayed multistates or by the coupling relationship of nonlinearity with the single delayed state, this paper makes the significant attempt to take into account both above issues simultaneously in the dynamic model (1). That is the novel time-delay situation that the nonlinearity (instead of linearity) is coupled with the delayed multistates (instead of single state).…”

Section: Remarkmentioning

confidence: 93%

“…Remark 1 The dynamic stochastic model with the time delays has been introduced to describe the dynamic behaviors of the mechanical systems such as the quarter-vehicle suspension and the robot manipulator in [1,2]. In these systems, time delays may result from the distributed nature of the system, material /energy transport, or from the time required to measure some of the variables, which are highly coupled with the nonlinearity as shown in (1).…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In these systems, time delays may result from the distributed nature of the system, material /energy transport, or from the time required to measure some of the variables, which are highly coupled with the nonlinearity as shown in (1). Fox example, in the quartervehicle suspension system with the second-order differential (see Fig.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…Nonlinear dynamic time-delay systems can be widely found in the practical applications such as the vehicle suspension system [1] and the robot manipulator [2]. Specially, the nonlinearity and time delay are usually coupled with each other in a very strong way, which contributes significantly to the complexity of system modeling.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian sum approximation filter for nonlinear dynamic time-delay system

Wang

Liang

2015

Nonlinear Dyn

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This paper focuses on designing Gaussian sum approximation filter for both accurately and rapidly estimating the state of a class of nonlinear dynamic time-delay systems. Firstly, a novel nonaugmented Gaussian filter (GF) is derived, whose superiority in computation efficiency is theoretically analyzed as compared to the standard augmented GF. Secondly, a nonaugmented Gaussian sum filter (GSF) is proposed to accurately capture the state estimates by a weight sum of the above-proposed GF. In GSF, each GF component is independent from the others and can be performed in a parallel manner so that GSF is conducive to high-performance computing across many compute nodes. Finally, the performance of the proposed GSF is demonstrated by a vehicle suspension system with time delay, where the GSF achieves higher accuracy than the single GF and is computationally much more efficient than the particle filter with the almost same accuracy.

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Section: Remarkmentioning

confidence: 93%

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaussian sum approximation filter for nonlinear dynamic time-delay system

Wang

Liang

2015

Nonlinear Dyn

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“…In recent years, a lot of solutions have been proposed to improve the suspension performance [2][3][4][5][6][7]. Among them, Kalman filtering algorithm for vehicle active suspension system with time-delays is proposed by Lee et al [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Song

Shin

2013

IET Control Theory & Applications

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This study presents a new robust filtering method in modelling an active multisensory suspension system with measurement delays and parameteric uncertainties in a state-space dynamical model. To achieve good performance of the system, a new distributed fusion receding horizon filtering frameworks are constructed to couple the continuous dynamics with the multisensory discrete measurements, and to coordinately deal with the parametric uncertainty and time-delays. The novel filtering algorithm is proposed based on the receding horizon strategy, standard mixed continuous-discrete Kalman filtering and discrete Kalman filtering for systems with time-delays in order to achieve high estimation accuracy and stability under parametric uncertainties. The key theoretical contributions of this study are the derivation of the error cross-covariance equations between the local receding horizon filters in order to compute the optimal matrix fusion weights. The high accuracy and efficiency of the new filter are demonstrated through its implementation and performance and then compared to the existing vehicle active suspension system.

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Gaussian/Gaussian‐mixture filters for non‐linear stochastic systems with delayed states

Wang

Liang

Huang

2014

IET Control Theory & Applications

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The Gaussian mixture approximation to the probability density function of the state is more appropriate than the single Gaussian approximation. A Gaussian mixture filter (GMF) is proposed for a class of non-linear discrete-time stochastic systems with the multi-state delayed case. First, a novel non-augmented filtering framework of the constituent Gaussian filter (GF) in GMF is derived, which recursively operates by analytical computation and non-linear Gaussian integrals. The implementation of such GF is thus transformed to the computation of such non-linear integrals in the proposed framework, which is solved by applying different numerical technologies for developing various variations of the non-augmented GF, for example, GF-cubature Kalman filter (CKF) based on the cubature rule. Secondly, a non-augmented GMF is discussed by a weight sum of the above proposed GF, where each GF component is independent from the others and can be performed in a parallel manner, and its corresponding weigh is updated by using the measurements according to Bayesian formula. Naturally, a variation or implementation of such GMF based on the cubature rule is the GMF-CKF. Finally, the performance of the new filters is demonstrated by a numerical example and a vehicle suspension estimation problem.

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Distributed Estimation Fusion With Application to a Multisensory Vehicle Suspension System With Time Delays

Cited by 12 publications

References 23 publications

Gaussian sum approximation filter for nonlinear dynamic time-delay system

Gaussian sum approximation filter for nonlinear dynamic time-delay system

Distributed mixed continuous‐discrete receding horizon filter for multisensory uncertain active suspension systems with measurement delays

Gaussian/Gaussian‐mixture filters for non‐linear stochastic systems with delayed states

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