scholarly journals Longwall Shearer Cutting Force Estimation

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
A. W. Reid ◽  
P. R. McAree ◽  
P. A. Meehan ◽  
H. Gurgenci
Keyword(s):  
Kalman Filter ◽  
Cutting Forces ◽  
Longwall Mining ◽  
Force Estimation ◽  
Underground Coal Mining ◽  
Indirect Measurements ◽  
Input Estimation ◽  
Internal Model Principle ◽  
Viable Approach ◽  
Augmented State

Longwall mining is an underground coal mining method that is widely used. A shearer traverses the coal panel to cut coal that falls to a conveyor. Operation of the longwall can benefit from knowledge of the cutting forces at the coal/shearer interface, particularly in detecting pick failures and to determine when the shearer may be cutting outside of the coal seam. It is not possible to reliably measure the cutting forces directly. This paper develops a method to estimate the cutting forces from indirect measurements that are practical to make. The structure of the estimator is an extended Kalman filter with augmented states whose associated dynamics encode the character of the cutting forces. The methodology is demonstrated using a simulation of a longwall shearer and the results suggest this is a viable approach for estimating the cutting forces. The contributions of the paper are a formulation of the problem that includes: the development of a dynamic model of the longwall shearer that is suitable for forcing input estimation, the identification of practicable measurements that could be made for implementation and, by numerical simulation, verification of the efficacy of the approach. Inter alia, the paper illustrates the importance of considering the internal model principle of control theory when designing an augmented-state Kalman filter for input estimation.

scholarly journals A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4495
Author(s):  
Rocco Adduci ◽  
Martijn Vermaut ◽  
Frank Naets ◽  
Jan Croes ◽  
Wim Desmet
Keyword(s):  
Kalman Filter ◽  
High Fidelity ◽  
Force Estimation ◽  
Computationally Efficient ◽  
Input Estimation ◽  
Multibody Model ◽  
System States ◽  
Filter Approach ◽  
The Stability ◽  
Stability And Accuracy

Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.

scholarly journals Parameters and Driving Force Estimation of Cell Motility via Expectation-Maximization (EM) Approach

2010 ◽  
Author(s):  
Dejan Milutinovic´ ◽  
Devendra P. Garg
Keyword(s):  
Stochastic Process ◽  
Kalman Filter ◽  
Immune System ◽  
Cell Motility ◽  
Expectation Maximization ◽  
Driving Force ◽  
Zebrafish Embryo ◽  
Force Estimation ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach

Motility is an important property of immune system cells. To describe cell motility, we use a continuous stochastic process and estimate its parameters and driving force based on a maximum likelihood approach. In order to improve the convergence of the maximization procedure, we use expectation-maximization (EM) iterations. The iterations include numerical maximization and the Kalman filter. To illustrate the method, we use cell tracks obtained from the intravital video microscopy of a zebrafish embryo.

scholarly journals Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Seyed Fakoorian ◽  
Vahid Azimi ◽  
Mahmoud Moosavi ◽  
Hanz Richter ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Continuous Time ◽  
Lyapunov Functions ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Reaction Force ◽  
Force Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

scholarly journals Solid Concentration Estimation by Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2657 ◽  
Author(s):  
Yongguang Tan ◽  
Shihong Yue
Keyword(s):  
Kalman Filter ◽  
Two Phase Flow ◽  
Estimation Method ◽  
Phase Flow ◽  
Solid Concentration ◽  
Two Phase ◽  
Indirect Measurements ◽  
Local Measurements ◽  
Tremendous Amount ◽  
Solid Liquid

One of the major tasks in process industry is solid concentration (SC) estimation in solid–liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation. Consequently, there is complementarity between them, and integrating direct and indirect measurements is helpful to improve the accuracy of SC estimation. In this paper, after recovering the interrelation of these measurements, we proposed a new SC estimation method according to Kalman filter fusion. Focusing on dredging engineering fields, SCs of representative flow pattern were tested. The results show that our proposed methods outperform the fused two types of measurements in real solid–liquid two-phase flow conditions. Additionally, the proposed method has potential to be applied to other fields as well as dredging engineering.

A Kalman Filter Based Tracking Scheme with Input Estimation

1979 ◽  
Vol AES-15 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Y.t. Chan ◽  
A.g.c. Hu ◽  
J.B. Plant
Keyword(s):  
Kalman Filter ◽  
Input Estimation
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