Application of Extended Kalman Filter in a Forced-Feedback Metering Poppet Valve System

2008 ◽  
Author(s):  
Chang Li ◽  
Roger Fales
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Linear Time ◽  
Control Volume ◽  
Load Pressure ◽  
Valve System ◽  
Poppet Valve ◽  
Parameter Variations ◽  
Measurement Noises ◽  
Optimal State Estimation

This work focuses on an accurate Extended Kalman Filter (EKF) estimator, which is applied in a forced-feedback metering poppet valve system (FFMPVS). The EKF estimator is used to estimate the position and velocity of the main poppet valve, the pilot poppet valve and the piston through using the control volume pressure, the load pressure and the pressure between the pilot poppet and the actuator housing, which are all disturbed by noise. The EKF estimator takes advantage of its recursive optimal state estimation to estimate the states of this metering poppet valve, which is a non-linear, time-variant dynamical system in real time. The EKF estimator has robustness to parameter variations and ability to filter measurement noises. It is shown that the EKF estimator tracks the states confidently and promptly for both the steady-state and transient performance, at the same time, the EKF estimator also filters the noise of the measured pressures.

Extended Kalman Filter–Based Pruning Method for Recurrent Neural Networks

1998 ◽  
Vol 10 (6) ◽  
pp. 1481-1505 ◽  
Author(s):  
John Sum ◽  
Lai-wan Chan ◽  
Chi-sing Leung ◽  
Gilbert H. Young
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Recurrent Neural Networks ◽  
Posterior Probability ◽  
Linear Time ◽  
Learning Methods ◽  
Pruning Method ◽  
Gradient Based

Pruning is one of the effective techniques for improving the generalization error of neural networks. Existing pruning techniques are derived mainly from the viewpoint of energy minimization, which is commonly used in gradient-based learning methods. In recurrent networks, extended Kalman filter (EKF)–based training has been shown to be superior to gradient-based learning methods in terms of speed. This article explains a pruning procedure for recurrent neural networks using EKF training. The sensitivity of a posterior probability is used as a measure of the importance of a weight instead of error sensitivity since posterior probability density is readily obtained from this training method. The pruning procedure is tested using three problems: (1) the prediction of a simple linear time series, (2) the identification of a nonlinear system, and (3) the prediction of an exchange-rate time series. Simulation results demonstrate that the proposed pruning method is able to reduce the number of parameters and improve the generalization ability of a recurrent network.

Data Assimilation of River Networks Using Extended Kalman Filter

2014 ◽  
Vol 580-583 ◽  
pp. 1923-1927
Author(s):  
Yi Fan Chen ◽  
Jing Lin Qian
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
River Network ◽  
Measurement Noise ◽  
River Networks ◽  
Network System ◽  
Process Noise ◽  
Measurement Noises

In order to improve the accuracy of river network hydraulic model, extended kalman filter was used for real-time updating model states. In a simulation example of a river network composed of 14 channels, it systematically analyzed the effects of process and measurement noises on state correction. The results show that the extended kalman filter is able to effectively carry out data assimilation of non-linear river network system, and big process noise in combination with relatively small measurement noise is recommended for state correction.

Synthesis of Cross-Correlation Functions of Partial Responses and the Extended Kalman Filter Approach for Structural Damage Detection Under Ambient Excitations

2018 ◽  
Vol 18 (08) ◽  
pp. 1840003 ◽  
Author(s):  
Y. Lei ◽  
D. D. Xia ◽  
F. Chen ◽  
Y. M. Deng
Keyword(s):  
Kalman Filter ◽  
Damage Detection ◽  
White Noise ◽  
Extended Kalman Filter ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Product Models ◽  
Structural Responses ◽  
Measurement Noises

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method.

A COMPARATIVE STUDY OF A SMOOTH VARIABLE STRUCTURE FILTER AND THE EXTENDED KALMAN FILTER

2008 ◽  
Vol 32 (3-4) ◽  
pp. 353-370
Author(s):  
Shu Wang ◽  
Saeid Habibi ◽  
Richard Burton
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Comparative Study ◽  
Extended Kalman Filter ◽  
Standard Technique ◽  
Variable Structure ◽  
System Model ◽  
Parameter Variations ◽  
Simulation Results ◽  
Smooth Variable Structure Filter

A new method of filtering strategy, referred to as the Smooth Variable Structure Filter (SVSF) is applied to the problem of state estimation on a class of nonlinear system. The SVSF is revised to reach the better estimation resolution. A comparative study is presented in which the Extended Kalman Filter (EKF) is applied to the same nonlinear system model. The estimation convergence and accuracy of the SVSF and EKF are comparable. The robustness of the SVSF to parameter variations is established through simulation results. This study is important because it allows the new SVSF to be critically compared to a standard technique such as the EKF.

scholarly journals Nonlinear state observers and extended Kalman filters for battery systems

2013 ◽  
Vol 23 (3) ◽  
pp. 539-556 ◽  
Author(s):  
Andreas Rauh ◽  
Saif S. Butt ◽  
Harald Aschemann
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Extended Kalman Filters ◽  
Battery Model ◽  
Finite Dimensional ◽  
State Observers ◽  
Parameter Variations ◽  
Estimation Procedures ◽  
Nonlinear State ◽  
Filtering Techniques

Abstract The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter variations are directly accessible by measurements. Moreover, this work provides a comparison of the performance of different observer and filtering techniques as well as a development of estimation procedures that guarantee a reliable detection of large parameter variations. For that reason, different charging and discharging current profiles of batteries are investigated by numerical simulations. The estimation procedures considered in this paper are, firstly, a nonlinear Luenberger-type state observer with an offline calculated gain scheduling approach, secondly, a continuous-time extended Kalman filter and, thirdly, a hybrid extended Kalman filter, where the corresponding filter gains are computed online.

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