Bayesian estimation via sequential Monte Carlo sampling—Constrained dynamic systems

Lang, Lixin; Chen, Wen-shiang; Bakshi, Bhavik R.; Goel, Prem K.; Ungarala, Sridhar

doi:10.1016/j.automatica.2007.02.012

Cited by 98 publications

(51 citation statements)

References 16 publications

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“…Gas path health parameters of turbofan engines vary within a certain range, therefore, the health parameter estimation is actually a state estimation problem with constraints. Kyriakides [44] and Lang [45] discuss how to accept/reject the particles in the PF based on constraint knowledge. The disadvantage is that it discards all the particles violating constraints, reduces the number of various particles and may yield poor estimations.…”

Section: The Constrained Ekpfmentioning

confidence: 99%

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring. OPEN ACCESSEnergies 2013, 6 493

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Section: The Constrained Ekpfmentioning

confidence: 99%

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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“…A great deal of interest is generated by the utility of these simple, accurate and fast algorithms for the generally infinite dimensional nonlinear filter [2,10,[19][20][21][22]. The central idea is to represent the non-Gauss ian densities by a large number of samples or particles distributed accordingly and update the samples and weights conditioned on measurement information according to Bayes rule.…”

Section: I't E Rp Is Iid Random Measurement Noise Vector Distributementioning

confidence: 99%

Computing arrival cost parameters in moving horizon estimation using sampling based filters

Ungarala

2009

Journal of Process Control

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“…Specifically in this paper, information about the road constraint is considered, which usually limits the vehicle's movement within a specified space defined by g(x t k ) ≤ 0 where g(·) is a possibly nonlinear function. To incorporate this inequality constraint, the sampling step is augmented with the acceptance-rejection process [9], which accepts the sample only if it satisfies the constraint. The corresponding constrained particle filter algorithm is summarised in Algorithm 1.…”

Section: Constrained Particle Filtermentioning

confidence: 99%

Coordinated standoff tracking of in- and out-of-surveillance targets using constrained particle filter for UAVs

Liu

Kim

et al. 2015

2015 IEEE Intelligent Vehicles Symposium (IV)

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Bayesian estimation via sequential Monte Carlo sampling—Constrained dynamic systems

Cited by 98 publications

References 16 publications

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Computing arrival cost parameters in moving horizon estimation using sampling based filters

Coordinated standoff tracking of in- and out-of-surveillance targets using constrained particle filter for UAVs

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