Application of the Improved BP Neural Network in Optimization Design of Control System of Simple Pendulum Surface Plate

2013 ◽  
Vol 718-720 ◽  
pp. 1682-1686
Author(s):  
Hong Zhou Zhang ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Large Angle ◽  
Plate Motion ◽  
Plate Movement ◽  
High Control ◽  
The Stability ◽  
Surface Plate

According to the characteristics of large angle of pendulum movement, in order to ensure the stability of the fixed plate motion pendulum, to analysis quantitatively the relation of oscillation angle and plate movement, it designed an automatic flat control system with STC89C52RC as the core of control chip, collected the information of oscillation angle by the sensor of angle,controls the angle of surface plate by advanced four phase stepper motor,uses the improved BP neural network to real-time correction in the software design part, the control system has high control precision, the stability and speedability of control system of surface plate were improved by measuring .

Research on Hydraulic Control System of Straightening Machine with Nine Rolls for Plates Based on BP Neural Network PID Controller

2013 ◽  
Vol 820 ◽  
pp. 117-121 ◽  
Author(s):  
Song Li ◽  
Jin Chun Song ◽  
Guan Gan Ren ◽  
Yan Cai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Mechanical Transmission ◽  
Hydraulic Control ◽  
Control Precision ◽  
Transmission Equipment ◽  
High Control ◽  
Neural Network Pid

A mechanical transmission equipment of traditional straightening machine for plates are driven by worm gear and worm, which causes small straightening force, slow pressing speed and low control precision. However, screwdown control system of straightening machine can be driven by hydraulic system, which will lead to large straightening force, rapid pressing speed and high control precision. This system was designed for straightening machine with nine rolls for plates, its transfer function was deduced, and the analysis on its stability and time response was conducted. A BP neural network PID controller was utilized in the system for improving dynamic characteristics. It can be concluded that the system responds rapidly, and stability and control precision are high if BP neural network PID controller is used in the system.

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

System of Annealing Furnace Temperature Control Based on Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 881-884
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nonlinear Approximation ◽  
Annealing Furnace ◽  
Furnace Temperature ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

In the thesis the adaptive ability of neural network strong and good nonlinear approximation ability, A controller is designed based on BP neural network by the adaptive ability of neural network strong and good nonlinear approximation ability in this paper, this method changed defect of the usual PID controller that parameters of annealing furnace condition are not easy set and the ability to adapt is poor. The new method is not only has good stability, but also has high control precision and strong adaptability.

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

scholarly journals Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Chun Yan ◽  
Meixuan Li ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Determination ◽  
Oil Analysis ◽  
Dissolved Gas ◽  
Adaboost Algorithm ◽  
Diagnosis Model ◽  
The Stability ◽  
Transformer Fault

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

scholarly journals Position Measurement Based on Fisheye Imaging

Proceedings ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 38
Author(s):  
Xianjing Li ◽  
Kun Li ◽  
Yanwen Chen ◽  
ZhongHao Li ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Imaging System ◽  
Large Angle ◽  
Image Features ◽  
Position Measurement ◽  
Fisheye Lens ◽  
Position Information ◽  
Object Distance ◽  
Nonlinear Imaging

For the omnidirectional measurement, the collected images of large-angle fisheye lens need to be corrected and spliced before next procedure, which is complicated and inaccurate. In this paper, a direct position measurement method based on fisheye imaging is proposed for large-angle imaging without any image correcting and splicing. A nonlinear imaging system of fisheye lens is used to acquire the sequence images based on its distortion model, and the critical distortion features of the sequence images are extracted, which contains the position information. And a BP neural network is trained with the extracted image features of previous standard experimental dataset. Finally, the trained BP neural network is employed to measure the object’s distance. Experimental results demonstrate show that the proposed method achieves simple close-object distance measurement with high robustness and a measurement error of ±0.5cm. The proposed method overcomes the shortcomings of conventional measurement methods and expands the fisheye applications filed for omnidirectional measurement.

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