scholarly journals A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

2019 ◽  
Vol 9 (14) ◽  
pp. 2850 ◽  
Author(s):  
Peng-Hao Hu ◽  
Ze-Xun Lu ◽  
Yuan-Qi Zhang ◽  
Shan-Lin Liu ◽  
Xue-Ming Dang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Magnetic Induction ◽  
Rotation Angle ◽  
Structural Parameters ◽  
Operation Time ◽  
Hall Sensor ◽  
Intensity Change ◽  
Ball Joint ◽  
Magnetic Induction Intensity

The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation orientation and angle. The basic idea is to embed a PM on a ball while the Hall sensors are arrayed into the ball socket. When the ball rotates, the Hall sensor array detects the variation of the magnetic induction intensity in space. By establishing a mathematical model between the variation of the magnetic induction intensity and the orientation and angle of rotation, the rotation angle in the space where the ball is located can be inversely solved. The establishment of the theoretical model is based on the theory of the equivalent magnetic charge method, which has a few native defects that cannot be overcome by itself. This paper presents the relationship between the magnetic induction intensity change and the rotation angle of the ball in space, which was constructed by an artificial neural network (ANN) and will simplify the mathematical model, shorten the operation time, and improve the efficiency of real-time detection. Based on the simulation analysis, the optimal matching scheme between the PM and the magnetic effect sensor was determined, and the structural parameters of the ball joint prototype were optimized. The data training and comparison test of the neural network model were completed on a self-developed calibration device. The experimental results show that for a ±20° measurement range, the average errors of the uniaxial measurements are 1′51″ and 1′55″ on the two axes, respectively. At present, the measurement accuracy of the prototype is still relatively low; however, this idea of modeling based on ANN removes the shackles of mathematical modeling, reminding us that we can consider the design of sensors or complete geometric measurement modeling from a new perspective.

scholarly journals Research on the 2-degree-of-freedom electromagnetic actuator in space

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775026 ◽  
Author(s):  
Rongqiang Liu ◽  
Fei Yang ◽  
Xingke Mu ◽  
Honghao Yue ◽  
Chenxi Zhu
Keyword(s):  
Theoretical Model ◽  
Magnetic Induction ◽  
Vibration Isolation ◽  
Structural Parameters ◽  
Magnetic Coupling ◽  
Degree Of Freedom ◽  
Electromagnetic Actuator ◽  
Working Principle ◽  
Electromagnetic Characteristics ◽  
Magnetic Induction Intensity

To meet space science experiments in micro-gravity environment, micro-spacecraft vibration isolation technology was developed in recent years. A new kind of 2-degree-of-freedom electromagnetic actuator based on Lorentz principle was developed in this article. The composition and working principle of the actuator were introduced, the parametric model for electromagnetic actuator was established, the structural optimization was carried out with the help of genetic algorithm in the MATLAB toolbox, and the final optimal structural parameters were obtained. For achieving the magnetic induction intensity in radial and axial directions, theoretical model for electromagnetic characteristics of the 2-degree-of-freedom actuator was established, and the expression for electromagnetic force was obtained. A static electric–magnetic coupling simulation of the whole actuator and also the experiments for measuring magnetic induction and magnetic force were carried out, and the correctness of theoretical model was verified.

scholarly journals Posture Dynamic Modeling and Stability Analysis of a Magnetic Driven Dual-Spin Spherical Capsule Robot

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 238
Author(s):  
Huiyuan Yang ◽  
Yongshun Zhang ◽  
Zhenhu Liu ◽  
Xu Liu ◽  
Guanxi Liu
Keyword(s):  
Angular Velocity ◽  
Magnetic Induction ◽  
Periodic Motion ◽  
Lyapunov Theory ◽  
Asymptotically Stable ◽  
Magnetic Torque ◽  
Stable Motion ◽  
Capsule Robot ◽  
Magnetic Induction Intensity ◽  
Spherical Capsule

In order to realize the intervention operation in the unstructured and ample environments such as stomach and colon, a dual-spin spherical capsule robot (DSCR) driven by pure magnetic torque generated by the universal rotating magnetic field (URMF) is proposed. The coupled magnetic torque, the viscoelastic friction torque, and the gravity torque were analyzed. Furthermore, the posture dynamic model describing the electric-magnetic-mechanical-liquid coupling dynamic behavior of the DSCR in the gastrointestinal (GI) tract was established. This model is a second-order periodic variable coefficient dynamics equation, which should be regarded as an extension of the Lagrange case for the dual-spin body system under the fixed-point motion, since the external torques were applied. Based on the Floquet–Lyapunov theory, the stability domain of the DSCR for the asymptotically stable motion and periodic motion were obtained by investigating the influence of the angular velocity of the URMF, the magnetic induction intensity, and the centroid deviation. Research results show that the DSCR can realize three kinds of motion, which are asymptotically stable motion, periodic motion, and chaotic motion, according to the distribution of the system characteristic multipliers. Moreover, the posture stability of the DSCR can be improved by increasing the angular velocity of the URMF and reducing the magnetic induction intensity.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Measurement of the magnetic induction vector in superconductors using a double-layer Hall sensor array

1998 ◽  
Vol 72 (22) ◽  
pp. 2891-2893 ◽  
Author(s):  
Y. Abulafia ◽  
M. McElfresh ◽  
A. Shaulov ◽  
Y. Yeshurun ◽  
Y. Paltiel ◽  
...  
Keyword(s):  
Double Layer ◽  
Magnetic Induction ◽  
Sensor Array ◽  
Hall Sensor ◽  
Induction Vector ◽  
Magnetic Induction Vector

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Longitudinal vibration analysis and suppression of electric wheel system driven by in-wheel motor considering unbalanced magnetic pull

2018 ◽  
Vol 233 (11) ◽  
pp. 2729-2745 ◽  
Author(s):  
Shuguang Zuo ◽  
Duoqiang Li ◽  
Yu Mao ◽  
Wenzhe Deng
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Dynamic Model ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Longitudinal Vibration ◽  
Structural Parameters ◽  
Torque Ripple ◽  
Longitudinal Dynamics ◽  
Unbalanced Magnetic Pull

With the blowout of electric vehicles recently, the key parts of the electric vehicles driven by in-wheel motors named the electric wheel system become the core of development research. The torque ripple of the in-wheel motor mainly results in the longitudinal dynamics of the electric wheel system. The excitation sources are first analyzed through the finite element method, including the torque ripple induced by the in-wheel motor and the unbalanced magnetic pull produced by the relative motion between the stator and rotor. The accuracy of the finite element model is verified by the back electromotive force test of the in-wheel motor. Second, the longitudinal-torsional coupled dynamic model is established. The proposed model can take into account the unbalanced magnetic pull. Based on the model, the modal characteristics and the longitudinal dynamics of the electric wheel system are analyzed. The coupled dynamic model is verified by the vibration test of the electric wheel system. Two indexes, namely, the root mean square of longitudinal vibration of the stator and the signal-to-noise ratio of the tire slip rate, are proposed to evaluate the electric wheel longitudinal performance. The influence of unbalanced magnetic pull on the evaluation indexes of the longitudinal dynamics is analyzed. Finally, the influence of motor’s structural parameters on the average torque, torque ripple, and equivalent electromagnetic stiffness are analyzed through the orthogonal test. A surrogate model between the structural parameters of the in-wheel motor and the average torque, torque ripple, and equivalent electromagnetic stiffness is established based on the Bp neural network. The torque ripple and the equivalent electromagnetic stiffness are then reduced through optimizing the structural parameters of the in-wheel motor. It turns out that the proposed Bp neural network–based method is effective to suppress the longitudinal vibration of the electric wheel system.

scholarly journals Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

2021 ◽  
pp. 70-74
Author(s):  
Iulia Clitan ◽  
◽  
Adela Puscasiu ◽  
Vlad Muresan ◽  
Mihaela Ligia Unguresan ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Web Application ◽  
Point Of View ◽  
The Novel ◽  
End User ◽  
First Case ◽  
Novel Coronavirus ◽  
Second Wave ◽  
Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

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