Computation of arrival cost for moving horizon estimation via unscented Kalman filtering

Qu, Cheryl C.; Hahn, Juergen

doi:10.1016/j.jprocont.2008.04.005

Cited by 88 publications

(50 citation statements)

References 10 publications

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“…Methods for formulating the arrival cost include EKF, UKF, or even discarding the arrival cost by taking it as a constant value. In this study, an UKF-based arrival cost calculation approach [36] is applied. Differently from the UKF described in Section 3.1, for the constrained problem, the selection of sigma points takes the parameter constraints into account so that none of the selected sigma points violate the boundaries of the state variables:…”

Section: Moving Horizon Estimationmentioning

confidence: 99%

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Zhang

Göhlich

2017

Applied Sciences

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Featured Application: The proposed estimators can be applied in many vehicle active safety systems. They are capable of estimating vehicle states that are normally not measurable or that are too complicated to measure directly using additional expensive sensors. Abstract:The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today's typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.

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Section: Moving Horizon Estimationmentioning

confidence: 99%

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Zhang

Göhlich

2017

Applied Sciences

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“…[15], [16]). However, under a linear model with Gaussian noise and no constraints the AC is derived in closed form as:…”

Section: Preliminariesmentioning

confidence: 99%

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

Timotheou¹,

Panayiotou²,

Polycarpou³

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Traffic state estimation is an important problem with significant applications in advanced traveler information systems, transportation management and traffic control. Nonetheless, the often faulty nature of measurement sensors, especially inductive loop detectors, hinders reliable state estimation. This work proposes a systematic, model-based, networkwide, moving-horizon approach for fault-tolerant traffic state estimation. By exploiting information redundancy and fault sparsity, it achieves reliable estimation and simultaneously detects, isolates and corrects measurements from periods of faulty sensor behavior. The approach is examined in relation to the Asymmetric Cell Transmission Model, a popular and powerful macroscopic first-order traffic flow model. In the absence of any faults, the proposed approach achieves similar results with other state-of-the-art estimation approaches while it can achieve better estimation performance when some sensors are faulty. It is further demonstrated that the developed approach can successfully handle multiple faults of different types.

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“…When using the MHE approach to this constrained problem, the constrained mode is used as the state estimate but the mean and covariance are typically left uncon strained by the suboptimal nonlinear filters used for arrival cost such as EKF, UKF and PF (Qu & Hahn, 2009;Robertson & Lee, 1995;Ungarala, 2009). The DSPF can remedy this situation by propagating a constrained arrival cost for MHE.…”

Section: N Imentioning

confidence: 99%

“…the use of particle filter to accurately propagate arrival cost parameters in MHE is also recommended as a means to keep the horizon length sma ll ( Ungarala, 2009). In related work the unscented Kalman filte r (UKF) is used fo r arrival cost (Qu & Hahn, 2009).…”

Section: Introductionmentioning

confidence: 99%

A direct sampling particle filter from approximate conditional density function supported on constrained state space

Ungarala

2011

Computers & Chemical Engineering

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Computation of arrival cost for moving horizon estimation via unscented Kalman filtering

Cited by 88 publications

References 10 publications

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Moving horizon fault-tolerant traffic state estimation for the Cell Transmission Model

A direct sampling particle filter from approximate conditional density function supported on constrained state space

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