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 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.

A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems

2013 ◽  
Vol 300-301 ◽  
pp. 623-626 ◽  
Author(s):  
Yong Zhou ◽  
Yu Feng Zhang ◽  
Ju Zhong Zhang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
Covariance Matrix ◽  
Unscented Kalman Filter ◽  
Square Root ◽  
Noise Covariance Matrix ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The Stability ◽  
Stability And Accuracy

This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on Square-Root Unscented Kalman Filter (SRUKF), the traditional Maybeck’s estimator is modified and extended to the nonlinear systems, the estimation of square root of the process noise covariance matrix Q or measurement noise covariance matrix R is obtained straightforwardly. Then the positive semi-definiteness of Q or R is guaranteed, some shortcomings of traditional Maybeck’s algorithm are overcome, so the stability and accuracy of the filter is improved greatly.

scholarly journals Rain Measurement Simulation Using the Hall Effect Flow-Meter

2020 ◽  
Vol 2 (2) ◽  
pp. 100-107
Author(s):  
Bernadus Herdi Sirenden
Keyword(s):  
Kalman Filter ◽  
Rainfall Intensity ◽  
Time Span ◽  
Measuring Instrument ◽  
Literature Study ◽  
Flow Meter ◽  
Measurement Results ◽  
The Stability ◽  
Stability And Accuracy ◽  
Rain Measurement

The sensor analysis or flow-meter measuring instrument has been successfully carried out on the signal output to see the stability or accuracy of a measurement. The flowmeter measurement value was analyzed using a rainfall simulator. The rainfall intensity  value will then be predicted using the Kalman filter. Kalman filters can predict various data  or output signals so that the measurement results can be more stable and accurate. This  research methodology consists of several stages, namely the stages of literature study, designing research tools and components, designing systems, making or assembling tools, testing all components, programs and testing the flow-meter output signal record. The flowmeter is controlled by the Arduino Nano microcontroller. Tests were carried out in this study ten times, with a time span of 60 seconds for each experiment. The increase in water flow was detected by the flow-meter which was then captured by the hercules application and the data was then copied to Ms. Excel. After the rainfall intensity value is obtained, the value will be estimated using the Kalman filter. The estimation results will show the stability and accuracy value of the flow-meter. 

Contact Force Estimation for an Elastic Beam Using Optimal High-Gain Disturbance Observer

2010 ◽  
Author(s):  
Yan Liu ◽  
Dirk So¨ffker
Keyword(s):  
Contact Force ◽  
Disturbance Observer ◽  
Elastic Beam ◽  
High Gain ◽  
Contact Forces ◽  
Force Estimation ◽  
Input Estimation ◽  
System States ◽  
Unknown Input Estimation ◽  
Noisy Measurements

This contribution presents a contact force estimation approach based on an optimal high-gain disturbance observer for an elastic beam using noisy measurements. The reconstruction of contact forces as an example for unknown input estimation represents a class of typical mechanical engineering problems related to the estimation of unknown effects for disturbance rejection or accommodation or fault diagnosis and isolation. The high-gain disturbance observers applied here is able to estimate estimate unknown external inputs together with system states. But choosing observer gains is a difficult task because of the influence of measurement noise. The important advantage of the proposed approach in comparison with classical high-gain disturbance observer is the self adjustment of the observer gains according to the actual estimation situation. Estimation results based on real measurements from known high-gain disturbance observer and the proposed optimal one are compared. It can be shown that the proposed algorithm allows optimized disturbance observer gains calculation, being able to be situatively adapted.

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

scholarly journals Explicit parallel co-simulation approach: analysis and improved coupling method based on H-infinity synthesis

2021 ◽  
Author(s):  
Weitao Chen ◽  
Shenhai Ran ◽  
Canhui Wu ◽  
Bengt Jacobson
Keyword(s):  
Frequency Domain ◽  
Wide Frequency Range ◽  
Coupling Method ◽  
Sampled Data ◽  
H Infinity ◽  
First Case ◽  
Communication Step ◽  
Coupling Variables ◽  
The Stability ◽  
Stability And Accuracy

AbstractCo-simulation is widely used in the industry for the simulation of multidomain systems. Because the coupling variables cannot be communicated continuously, the co-simulation results can be unstable and inaccurate, especially when an explicit parallel approach is applied. To address this issue, new coupling methods to improve the stability and accuracy have been developed in recent years. However, the assessment of their performance is sometimes not straightforward or is even impossible owing to the case-dependent effect. The selection of the coupling method and its tuning cannot be performed before running the co-simulation, especially with a time-varying system.In this work, the co-simulation system is analyzed in the frequency domain as a sampled-data interconnection. Then a new coupling method based on the H-infinity synthesis is developed. The method intends to reconstruct the coupling variable by adding a compensator and smoother at the interface and to minimize the error from the sample-hold process. A convergence analysis in the frequency domain shows that the coupling error can be reduced in a wide frequency range, which implies good robustness. The new method is verified using two co-simulation cases. The first case is a dual mass–spring–damper system with random parameters and the second case is a co-simulation of a multibody dynamic (MBD) vehicle model and an electric power-assisted steering (EPAS) system model. Experimental results show that the method can improve the stability and accuracy, which enables a larger communication step to speed up the explicit parallel co-simulation.

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