scholarly journals Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhang Bing ◽  
Wang Xiaodong ◽  
Lu Hao ◽  
Hao Zhaojun ◽  
Gu Changchao
Keyword(s):  
Nonlinear Filtering ◽  
Simulation Analysis ◽  
Large Angle ◽  
Error Model ◽  
Statistical Characteristics ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Nonlinear Error ◽  
Estimation Precision ◽  
The Stability

When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

scholarly journals Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoyan Chen ◽  
Qiuju Zhang ◽  
Yilin Sun
Keyword(s):  
Calibration Method ◽  
Industrial Applications ◽  
Error Model ◽  
Robot Calibration ◽  
Positioning Accuracy ◽  
Nonlinear Error ◽  
Nonlinear Compensation ◽  
The Stability ◽  
Stability And Accuracy ◽  
Linear Piecewise

This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).

scholarly journals Vibration Error Correction for the FOGs-Based Measurement in a Drilling System Using an Extended Kalman Filter

2021 ◽  
Vol 11 (14) ◽  
pp. 6514
Author(s):  
Lu Wang ◽  
Yuanbiao Hu ◽  
Tao Wang ◽  
Baolin Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Original Data ◽  
Calibration Method ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling ◽  
Drilling System ◽  
Vibration Error

Fiber-optic gyroscopes (FOGs)-based Measurement While Drilling system (MWD) is a newly developed instrument to survey the borehole trajectory continuously and in real time. However, because of the strong vibration while drilling, the measurement accuracy of FOG-based MWD deteriorates. It is urgent to improve the measurement accuracy while drilling. Therefore, this paper proposes an innovative scheme for the vibration error of the FOG-based MWD. Firstly, the nonlinear error models for the FOGs and ACCs are established. Secondly, a 36-order Extended Kalman Filter (EKF) combined with a calibration method based on 24-position is designed to identify the coefficients in the error model. Moreover, in order to obtain a higher accurate error model, an iterative calibration method has been suggested to suppress calibration residuals. Finally, vibration experiments simulating the drilling vibration in the laboratory is implemented. Compared to the original data, compensated the linear error items, the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While compensating for the nonlinear error items of the FOG-based MWD, the error of 3D borehole trajectory can be reduced by a ratio from 44.13% to 97.22%. In conclusion, compensation of the nonlinear error of FOG-based MWD could improve the trajectory survey accuracy under vibration.

scholarly journals Numerical Simulation on Large Deformation of Weak Rock Mass Tunnel Under High Geostress

2018 ◽  
Vol 175 ◽  
pp. 03025
Author(s):  
Feng Zhou ◽  
Hongjian Jiang ◽  
Xiaorui Wang
Keyword(s):  
Rock Mass ◽  
Large Deformation ◽  
Lateral Pressure ◽  
Simulation Analysis ◽  
Pressure Coefficient ◽  
Surrounding Rock ◽  
Analog Simulation ◽  
Lateral Pressure Coefficient ◽  
High Geostress ◽  
The Stability

The problem about the stability of tunnel surrounding rock is always an important research object of geotechnical engineering, and the right or wrong of the result from stability analysis on surrounding rock is related to success or failure of an underground project. In order to study the deformation rules of weak surrounding rock along with lateral pressure coefficient and burying depth varying under high geostress and discuss the dynamic variation trend of surrounding rock, the paper based on the application of finite difference software of FLAC3D, which can describe large deformation character of rock mass, analog simulation analysis of surrounding rock typical section of the class II was proceeded. Some conclusions were drawn as follows: (1) when burying depth is invariable, the displacements of tunnel surrounding rock have a trend of increasing first and then decreasing along with increasing of lateral pressure coefficient. The floor heave is the most sensitive to change of lateral pressure coefficient. The horizontal convergence takes second place. The vault subsidence is feeblish to change of lateral pressure coefficient. (2) The displacements of tunnel surrounding rock have some extend increase along with increasing of burying depth. The research conclusions are very effective in analyzing the stability of surrounding rock of Yunling tunnel. These are going to be a reference to tunnel supporting design and construction.

scholarly journals Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4105 ◽  
Author(s):  
Qiuying Wang ◽  
Juan Yin ◽  
Aboelmagd Noureldin ◽  
Umar Iqbal
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Rapid Development ◽  
Alignment Algorithm ◽  
Magnetic Disturbance ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Positioning Systems ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm

Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) based on Micro Inertial Measurement Units (MIMUs), have recently attracted widespread attention with the rapid development of MIMUs. The can be used in challenging environments such as firefighting and the military, even without augmenting with Global Navigation Satellite System (GNSS). Zero Velocity Update (ZUPT) provides a solution for the accumulated positioning errors produced by the low precision and high noise of the MIMU, however, there are some problems using ZUPT for FIPPS, include fast-initial alignment and unobserved heading misalignment angle, which are addressed in this paper. Our first contribution is proposing a fast-initial alignment algorithm for foot-mounted inertial/magnetometer pedestrian positioning based on the Adaptive Gradient Descent Algorithm (AGDA). Considering the characteristics of gravity and Earth’s magnetic field, measured by accelerometers and magnetometers, respectively, when the pedestrian is standing at one place, the AGDA is introduced as the fast-initial alignment. The AGDA is able to estimate the initial attitude and enhance the ability of magnetic disturbance suppression. Our second contribution in this paper is proposing an inertial/magnetometer positioning algorithm based on an adaptive Kalman filter to solve the problem of the unobserved heading misalignment angle. The algorithm utilizes heading misalignment angle as an observation for the Kalman filter and can improve the accuracy of pedestrian position by compensating for magnetic disturbances. In addition, introducing an adaptive parameter in the Kalman filter is able to compensate the varying magnetic disturbance for each ZUPT instant during the walking phase of the pedestrian. The performance of the proposed method is examined by conducting pedestrian test trajectory using MTi-G710 manufacture by XSENS. The experimental results verify the effectiveness and applicability of the proposed method.

scholarly journals Application to Engineering and Medical Data Using Three-Parameter Exponential Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Waleed Almutiry ◽  
Amani Abdullah Alahmadi ◽  
Ibrahim Elbatal ◽  
Ibrahim E. Ragab ◽  
Oluwafemi Samson Balogun ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Residual Life ◽  
Quantile Function ◽  
Exponential Model ◽  
Statistical Characteristics ◽  
Mean Residual Life ◽  
New Model ◽  
Conditional Moments ◽  
Mean Inactivity Time ◽  
Real World Datasets

This paper is devoted to a new lifetime distribution having three parameters by compound the exponential model and the transmuted Topp-Leone-G. The new proposed model is called the transmuted Topp-Leone exponential model; it is useful in lifetime data and reliability. The new model is very flexible; its pdf can be right skewness, unimodal, and decreasing shaped, but the hrf of the suggested model can be unimodal, constant, and decreasing. Numerous statistical characteristics of the new model, notably the quantile function, moments, incomplete moments, conditional moments, mean residual life, mean inactivity time, and entropy are produced and investigated. The system’s parameters are estimated using the maximum likelihood approach. All estimators should be theoretically convergent, which is supported by a simulation analysis. Finally, two real-world datasets from the engineering and medical disciplines explore the new model’s relevance and adaptability in comparison to the alternatives models such as the beta exponential, the Marshall–Olkin generalized exponential, the exponentiated Weibull, the modified Weibull, and the transmuted Burr type X models.

scholarly journals Moving State Marine SINS Initial Alignment Based on High Degree CKF

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong-Gang Zhang ◽  
Yu-Long Huang ◽  
Zhe-Min Wu ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Alignment Method ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Central Difference ◽  
Angle Condition ◽  
Cubature Kalman Filter ◽  
High Degree ◽  
Central Difference Kalman Filter

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.

scholarly journals Study of Caspase 8 Inhibition for the Management of Alzheimer’s Disease: A Molecular Docking and Dynamics Simulation

Molecules ◽  
2020 ◽  
Vol 25 (9) ◽  
pp. 2071
Author(s):  
Syed Sayeed Ahmad ◽  
Meetali Sinha ◽  
Khurshid Ahmad ◽  
Mohammad Khalid ◽  
Inho Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Md Simulation ◽  
Simulation Analysis ◽  
Dynamics Simulation ◽  
Caspase 8 ◽  
Amino Acid Residues ◽  
The Stability ◽  
Molecular Docking And Dynamics

Alzheimer’s disease (AD) is the most common type of dementia and usually manifests as diminished episodic memory and cognitive functions. Caspases are crucial mediators of neuronal death in a number of neurodegenerative diseases, and caspase 8 is considered a major therapeutic target in the context of AD. In the present study, we performed a virtual screening of 200 natural compounds by molecular docking with respect to their abilities to bind with caspase 8. Among them, rutaecarpine was found to have the highest (negative) binding energy (−6.5 kcal/mol) and was further subjected to molecular dynamics (MD) simulation analysis. Caspase 8 was determined to interact with rutaecarpine through five amino acid residues, specifically Thr337, Lys353, Val354, Phe355, and Phe356, and two hydrogen bonds (ligand: H35-A: LYS353:O and A:PHE355: N-ligand: N5). Furthermore, a 50 ns MD simulation was conducted to optimize the interaction, to predict complex flexibility, and to investigate the stability of the caspase 8–rutaecarpine complex, which appeared to be quite stable. The obtained results propose that rutaecarpine could be a lead compound that bears remarkable anti-Alzheimer’s potential against caspase 8.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

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