Chain State Monitoring for A Heavy Scraper Conveyor Using UWB-based Extended Kalman Filter Technique with Range Constraint Selection Method

2021 ◽  
pp. 1-1
Author(s):  
Yilian Hua ◽  
Zhencai Zhu ◽  
Gongbo Zhou ◽  
Gang Shen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Selection Method ◽  
State Monitoring ◽  
Filter Technique ◽  
Scraper Conveyor ◽  
Constraint Selection

Extended Kalman Filter for Reconstruction of a Nonlinear Thermofluid System

1986 ◽  
Vol 108 (2) ◽  
pp. 156-158 ◽  
Author(s):  
R. Shoureshi ◽  
K. McLaughlin
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Heat Pump ◽  
Extended Kalman Filter ◽  
Eigenvalue Distribution ◽  
Integration Time ◽  
Time Step ◽  
Filter Technique ◽  
Gain Matrix ◽  
Good Agreement

Extended Kalman filter technique is used to develop an observer for a nonlinear thermofluid system, namely a heat pump. The observer’s optimal gain matrix is designed based on the eigenvalue distribution, integration time step, and stability of the system along a desired trajectory. The observer response is compared with experimental data and very good agreement is obtained.

scholarly journals Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987464 ◽  
Author(s):  
Cong Hung Do ◽  
Huei-Yung Lin
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Optimization Technique ◽  
Computational Cost ◽  
Simultaneous Localization And Mapping ◽  
Mobile Robot Navigation ◽  
Filter Technique ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline using a particle swarm optimization technique. With this approach, the advantages of high-dimensional search space can be exploited and more effective training can be achieved. In the experiments, the mobile robot navigation with a number of landmarks under two benchmark situations is evaluated. The results have demonstrated that the proposed adaptive neuro-fuzzy extended Kalman filter technique provides the improvement in both performance efficiency and computational cost.

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