Force Ripple Suppression Research for Linear Motor Servo System Based on BP Neural Network

2014 ◽  
Vol 654 ◽  
pp. 191-195
Author(s):  
Rui Peng ◽  
Yue Nan Zeng ◽  
Jian Tang ◽  
Kang Ping Chen
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Theoretical Analysis ◽  
Permanent Magnet ◽  
Bp Neural Network ◽  
Detent Force ◽  
Simulation Results ◽  
Force Ripple ◽  
The Mathematical Model ◽  
Suppression Strategy

For the permanent magnet synchronous linear motor’s (PMSLM) force ripple, the mathematical model of detent force is established in this paper, and presents a suppression strategy based on neural network. By the designing of BP neural network force ripple observer, theoretical analysis shows can effectively restrain the force ripple. Simulation results show the correctness and validity of the suppression strategy.

The Application of Double Closed-Loop Unit Gain Negative Feedback and BP Neural Network Controller in Single Inverted Pendulum

2012 ◽  
Vol 490-495 ◽  
pp. 1723-1727
Author(s):  
Jun Ting Wang ◽  
Guo Ping Liu ◽  
Wei Jin ◽  
Gen Fu Xiao
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Negative Feedback ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Inverted Pendulum ◽  
Root Locus ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Mathematical Model

In the paper the mathematical model of the single inverted pendulum is established, on the base of the root locus and the control tasks the control system is made up of double closed-loop unit gain negative feedback and BP neural network controller. The results show that the inverted pendulum is efficiently controlled.

Application of the collision mathematical model based on a BP neural network in railway vehicles

2020 ◽  
pp. 095440972095986
Author(s):  
Yu-Ru Li ◽  
Tao Zhu ◽  
Shou-Ne Xiao ◽  
Bing Yang ◽  
Guang-Wu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Finite Element ◽  
Bp Neural Network ◽  
Learning Performance ◽  
Small Data ◽  
Data Set ◽  
Computation Efficiency ◽  
Simulation Results ◽  
Relative Errors

In order to enhance the learning performance of small-data-set models and improve the computation efficiency of finite element simulations of vehicle collision, the collision mathematical model (VCMM) based on the back-propagation (BP) neural network is established to predict the collision response data of a single car and marshalling cars at unknown velocities. The predicted results of VCMM were compared with the simulation results of the finite element method (FEM) to verify the model. The compared results show that the maximum relative errors of deformation, energy absorption and average interfacial force of a single vehicle are all below 8.5%, and the relative errors of the maximum compression of the C0 coupler and the internal energy of the A1 car among the marshalling cars are all less than 5%. In addition, the calculation time of the single car and marshalling cars collisions based on the VCMM are reduced by 24.36 and 61.8 times, respectively, compared with the FEM results, and the simulation calculation efficiency is greatly improved. The prediction result of VCMM will partially replace experimental and simulation results for crashworthiness and safety design of the vehicle structure in future studies.

Research on Control System of Gearless Permanent Magnet Synchronous Traction Elevator

2011 ◽  
Vol 383-390 ◽  
pp. 5945-5950
Author(s):  
Yan Hu ◽  
Zhen Guang ◽  
Xiao Yu Wang
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Permanent Magnet ◽  
Pulse Width ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Modulation Method ◽  
Driving System ◽  
Simulation Results ◽  
The Mathematical Model

A driving system for gearless traction machine plays an very important role in controlling elevator’s running. And its performances have a direct effect on the elevator’s performance. On the basic of the mathematical model of the gearless permanent magnetic synchronous machine (PMSM), id=0 vector control method and space vector pulse width modulation method are used in the control system. Then making a simulation on the system designed by MATLAB/SIMULINK. The simulation results show that the control method is feasible.

The Simulation Study of Asynchronous Motor Based on dq Coordinate System

2013 ◽  
Vol 441 ◽  
pp. 484-487
Author(s):  
Yi Ming Li ◽  
Jun Rong
Keyword(s):  
Mathematical Model ◽  
Theoretical Analysis ◽  
Coordinate System ◽  
Simulation Study ◽  
Control Method ◽  
Asynchronous Motor ◽  
Simulation Models ◽  
Solid Foundation ◽  
Simulation Results ◽  
The Mathematical Model

With the improvement of modem science and technology, asynchronous motor plays more and more important role in modem industrial life, and asynchronous motor has the broadest application and requirement in all kind of motors. The paper firstly builds the mathematical model of asynchronous motor in dq coordinate system, then establishes the simulation models of asynchronous motor based on Matlab/Simulink. The last the paper gives the simulation results and analyzes the simulation results. The results prove that the theoretical analysis of asynchronous motor in dq coordinate system is correct completely, and it lays a solid foundation for the study of control method for asynchronous motor.

Research on Dynamic Balance Method of Precision Centrifuge

2014 ◽  
Vol 945-949 ◽  
pp. 777-780
Author(s):  
Tao Liu ◽  
Yong Xu ◽  
Bo Yuan Mao
Keyword(s):  
Mathematical Model ◽  
Balance Control ◽  
Dynamic Balance ◽  
Dynamic Balancing ◽  
Structure Characteristics ◽  
Balance System ◽  
Simulation Results ◽  
Set Up ◽  
System Controller ◽  
The Mathematical Model

Firstly, according to the structure characteristics of precision centrifuge, the mathematical model of its dynamic balancing system was set up, and the dynamic balancing scheme of double test surfaces, double emendation surfaces were established. Then the dynamic balance system controller of precision centrifuge was designed. Simulation results show that the controller designed can completely meet the requirements of precision centrifuge dynamic balance control system.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

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.

The Impact Analysis of Revolute Pair Clearance to the End Pose Accuracy of Welding Robot

2015 ◽  
Vol 778 ◽  
pp. 259-263
Author(s):  
Fa Jun Zhang ◽  
Lin Zi Li ◽  
Hui Lin ◽  
Yin Lin Pu ◽  
Zhu Xin
Keyword(s):  
Mathematical Model ◽  
Impact Analysis ◽  
Joint Clearance ◽  
Welding Robot ◽  
Movement Characteristic ◽  
Movement Stability ◽  
Simulation Results ◽  
The Impact ◽  
The Mathematical Model ◽  
Welding Position

Various uncertain factors affect the movement of the welding robot, thus welding gun tend to deviate from the theory of welding position which reduces the welding accuracy, of which the revolute pair clearance have an greater effect on the movement of the welding robot. In order to study the influence of revolute pair clearance to the end pose accuracy of welding robot, the mathematical model of revolute pair clearance was established, and the software SolidWorks was used for establishing the welding robot model, making simulations of the mechanical arm with joint clearance and no joint clearance. At last, the movement characteristic of the hinge shaft is attained. The simulation results showed that the shaft velocity and displacement of mechanical arm with joint clearance has a certain degree of fluctuation, which affecting the end pose accuracy of welding robot , and reducing the movement stability and the welding accuracy of welding robot.

scholarly journals A modified NK algorithm based on BP neural network and DEMATEL for evolution path optimization of urban innovation ecosystem

2021 ◽  
Author(s):  
Ruijian Liu ◽  
Fangcheng Tang ◽  
Yuhan Wang ◽  
Shaofang Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Governance ◽  
Innovation Network ◽  
Governance System ◽  
Innovation Ecosystem ◽  
Governance Capacity ◽  
New Era ◽  
Evolution Path ◽  
Simulation Results

AbstractIn the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of “how the urban innovation ecosystem evolves”, this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects.

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.

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