16,933 research outputs found
Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter
Ensemble Kalman filters are widely used for data assimilation applications in the geosciences. While they are remarkably stable even with nonlinear systems, it is known that they are not optimal in this case. The alternative particle filters are fully nonlinear, but difficult to apply with high-dimensional models. To combine the strengths of both filter types, a hybrid filter is introduced that combines the local ensemble transform Kalman filter (LETKF) with the nonlinear ensemble transform filter (NETF). Three variants of the hybrid filter are formulated. The hybridization is controlled by a hybrid weight. Different hybrid weights are examined and a new adaptive approach based on the ensemble skewness and kurtosis is introduced. The different hybrid filters and the schemes to compute the hybrid weight are assessed in numerical experiments with the nonlinear Lorenz-63 and Lorenz-96 models at different degrees of nonlinearity. A hybrid variant that first applies the NETF followed by the LETKF yields the best results. For the Lorenz-96 model, error reductions by up to 21.5% compared with the LETKF are obtained for the same ensemble size. Computing the hybrid weight based on skewness and kurtosis combined with the effective sample size yields the lowest estimation errors and the overall highest stability of the hybrid filter. The new hybrid filter applies localization and inflation and is hence also usable with high-dimensional models and can potentially provide a robust way to account for leading nonlinearity with small ensembles in nonlinear data assimilation applications
Integration of a failure monitoring within a hybrid dynamic simulation environment
The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering
Dynamic state reconciliation and model-based fault detection for chemical processes
In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe
Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems
In this paper, a novel feedback control-based particle filter algorithm for
the continuous-time stochastic hybrid system estimation problem is presented.
This particle filter is referred to as the interacting multiple model-feedback
particle filter (IMM-FPF), and is based on the recently developed feedback
particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for
each discrete mode, and an exact filter recursion for the mode association
probability. The proposed IMM-FPF represents a generalization of the
Kalmanfilter based IMM algorithm to the general nonlinear filtering problem.
The remarkable conclusion of this paper is that the IMM-FPF algorithm retains
the innovation error-based feedback structure even for the nonlinear problem.
The interaction/merging process is also handled via a control-based approach.
The theoretical results are illustrated with the aid of a numerical example
problem for a maneuvering target tracking application
Direct yaw-moment control of an in-wheel-motored electric vehicle based on body slip angle fuzzy observer
A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental settin
Comparison of reduced-order, sequential and variational data assimilation methods in the tropical Pacific Ocean
This paper presents a comparison of two reduced-order, sequential and
variational data assimilation methods: the SEEK filter and the R-4D-Var. A
hybridization of the two, combining the variational framework and the
sequential evolution of covariance matrices, is also preliminarily investigated
and assessed in the same experimental conditions. The comparison is performed
using the twin-experiment approach on a model of the Tropical Pacific domain.
The assimilated data are simulated temperature profiles at the locations of the
TAO/TRITON array moorings. It is shown that, in a quasi-linear regime, both
methods produce similarly good results. However the hybrid approach provides
slightly better results and thus appears as potentially fruitful. In a more
non-linear regime, when Tropical Instability Waves develop, the global nature
of the variational approach helps control model dynamics better than the
sequential approach of the SEEK filter. This aspect is probably enhanced by the
context of the experiments in that there is a limited amount of assimilated
data and no model error
First stage of LISA data processing: Clock synchronization and arm-length determination via a hybrid-extended Kalman filter
In this paper, we describe a hybrid-extended Kalman filter algorithm to
synchronize the clocks and to precisely determine the inter-spacecraft
distances for space-based gravitational wave detectors, such as (e)LISA.
According to the simulation, the algorithm has significantly improved the
ranging accuracy and synchronized the clocks, making the phase-meter raw
measurements qualified for time- delay interferometry algorithms.Comment: 14 pages, Phys. Rev. D 90, 064016 (2014
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