Estimation of Lateral Track Irregularity Through Kalman Filtering Techniques

Muñoz, Sergio; Ros, Javier; Urda, Pedro; Escalona, José L.

doi:10.1109/access.2021.3073606

Cited by 20 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many research scholars used model-based estimation techniques by applying simple Kalman filter to estimate the parameters of railway wheelset [2,8,9,23,24]. But, a single Kalman filter, due to the nonlinear behavior of railway dynamics, is not suitable for wheel-track interaction system.…”

Section: Estimator Design For Railway Wheelset Parametersmentioning

confidence: 99%

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

Mal¹,

Memon²,

Kalwar³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad vehicle. The proper estimation of adhesion conditions and their real-time implementation is considered a challenge for scholars. In this paper, the development of simulation model of extended Kalman filter (EKF) in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of track. Due to concurrent in nature, the Xilinx ® System-on-Chip Zynq Field Programmable Gate Array (FPGA) device is chosen to check the onboard estimation of wheel-rail interaction parameters by using the National Instruments (NI) myRIO ® development board. The NI myRIO ® development board is flexible to deal with nonlinearities, uncertain changes, and fastchanging dynamics in real-time occurring in wheel-rail contact conditions during vehicle operation. The simulated dataset of the railway nonlinear wheelset model is tested on FPGA-based EKF with different track conditions and with accelerating and decelerating operations of the vehicle. The proposed model-based estimation of railway wheelset parameters is synthesized on FPGA and its simulation is carried out for functional verification on FPGA. The obtained simulation results are aligned with the simulation results obtained through MATLAB. To the best of our knowledge, this is the first time study that presents the implementation of a model-based estimation of railway wheelset parameters on FPGA and its functional verification. The functional behavior of the FPGA-based estimator shows that these results are the addition of current knowledge in the field of the railway.

show abstract

Section: Estimator Design For Railway Wheelset Parametersmentioning

confidence: 99%

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

Mal¹,

Memon²,

Kalwar³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…However, due to errors, the navigation data used to obtain the spatial position accuracy of UAVs frequently fall outside of this range. Kalman Filters (KF), Unscented Kalman Filters (UKF), and Extended Kalman Filters (EKF) are commonly used to achieve better parameter estimation in linear and nonlinear estimation systems, respectively [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. KF-based parameter selection can greatly influence the estimation performance [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Near-Field Beamforming Algorithms for UAVs

Zhang¹,

Wang²,

Peng³

et al. 2023

Sensors

View full text Add to dashboard Cite

This study presents three distributed beamforming algorithms to address the challenges of positioning and signal phase errors in unmanned aerial vehicle (UAV) arrays that hinder effective beamforming. Firstly, the array’s received signal phase error model was analyzed under near-field conditions. In the absence of navigation data, a beamforming algorithm based on the Extended Kalman Filter (EKF) was proposed. In cases where navigation data were available, Taylor expansion was utilized to simplify the model, the non-Gaussian noise of the compensated received signal phase was approximated to Gaussian noise, and the noise covariance matrix in the Kalman Filter (KF) was estimated. Then, a beamforming algorithm based on KF was developed. To further estimate the Gaussian noise distribution of the received signal phase, the noise covariance matrix was iteratively estimated using unscented transformation (UT), and here, a beamforming algorithm based on the Unscented Kalman Filter (UKF) was proposed. The proposed algorithms were validated through simulations, illustrating their ability to suppress the malign effects of errors on near-field UAV array beamforming. This study provides a reference for the implementation of UAV array beamforming under varying conditions.

show abstract

“…The filter-based estimation of the wheel-rail interface force was first proposed in [6] using the extended Kalman filter, which was further developed in [7] as a nonlinear estimator and applied in [8] to estimate the lateral track irregularities. The advantage of the extended Kalman filter lies in that the parameters of the vehicle system can be estimated separately, with the help of a linearized model of the system.…”

Section: Introductionmentioning

confidence: 99%

Estimation of wheel–rail structural interactions from motion signals of high-speed train bogie

Zhao

Huang

Sun

2022

Int. J. Dynam. Control

View full text Add to dashboard Cite

The wheel–rail contact forces are responsible for hunting instability of high-speed train and fatigue damage of the wheel and rail. The wheel–rail contact forces are usually nonlinear, motion-dependent, time-varying and destabilizing, and cannot be directly measured when the train is in motion. In this paper, we present a useful tool for engineers to estimate the wheel–rail contact forces from train motion signals. In particular, an extended state observer is adopted to estimate the wheel–rail contact forces of a high-speed train bogie from its motion measurements. We make use of the condition for observability of the extended state observer to investigate the design of motion sensors in terms of the needed number and types. The accurate estimated wheel–rail contact forces together with the corresponding motion measurements are then used to estimate parameters of a wheel–rail contact force model with the help of recursive least squares algorithm. Extensive numerical simulations are presented to demonstrate and validate the proposed method. The research concept and proposed method of this paper are innovative. The results indicate that the extended state observer delivers excellent estimation performance for the bogie with a limited number of motion measurements. The new findings of the paper can lead to the development of new tools for engineers to use to monitor the health of wheel and rail from motion signals of the train.

show abstract

Estimation of Lateral Track Irregularity Through Kalman Filtering Techniques

Cited by 20 publications

References 22 publications

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

Near-Field Beamforming Algorithms for UAVs

Estimation of wheel–rail structural interactions from motion signals of high-speed train bogie

Contact Info

Product

Resources

About