scholarly journals Punctuality Algorithm Based on BP Neural Network PID Control

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Yingying Yang ◽  
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Bp Neural Network ◽  
Pid Control ◽  
Information Society ◽  
Crystal Oscillator ◽  
Satellite Clock ◽  
The Core ◽  
Core Idea ◽  
Neural Network Pid

In the modern information society, high-precision clocks are particularly important in the fields of electric power, communications, aviation, and finance, and have very strict objective requirements in terms of frequency accuracy. Currently, the technology of using GPS satellite clock sources to synchronize local clocks has become one of the mainstream methods for generating high-precision clocks at home and abroad. The core idea of this technology is to use the satellite clock to tame the local clock. Due to the development and application of 5G, the accuracy of the system's punctuality have higher requirements, Through analysis, it is found that the combination of BP neural network and PID control can be used to optimize the control of the constant temperature crystal oscillator and improve the precision of punctuality. Finally, the simulation results show that the method has a significant effect in improving the accuracy of punctuality.

scholarly journals Study on Computer Monitoring System for Oilfield Joint Stations Based on the BP Neural Network PID Control Algorithm

2021 ◽  
Author(s):  
Jingtian Xu ◽  
yanli qiao
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
The Core ◽  
Computer Monitoring ◽  
Three Phase ◽  
Phase Separator ◽  
Neural Network Pid

Abstract: The Hanqu Joint Station of the Dingbian Oil Production Plant of Yanchang Oilfield Co., Ltd is located at the edge of the desert in northern China. the bad field conditions and strong sandstorm, the hardware of computer monitoring system of Joint Station is often damaged. At the same time, the core equipment of the joint station three-phase separator oil chamber liquid level is hard to achieve high precision constant value control, the general control algorithm is difficult to meet the control requirements.This paper proposed a design scheme of a oilfield joint station computer monitoring system based on the Siemens S7-300 PLC, the hardware of the monitoring system adopts the redundancy scheme of dual monitoring computers, dual programmable logic controllers (PLCs), and dual industrial Ethernet. The BP neural network PID control algorithm was used to realize constant value control of the oil chamber liquid level of the three-phase separator of the core equipment of the joint station，and realized high control precision. The monitoring system could well adapt to the harsh environment of the scene, and showed high reliability and efficiency.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

scholarly journals Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

2018 ◽  
Vol 108 ◽  
pp. 042056
Author(s):  
Jinzhi Ren ◽  
Wei Xiang ◽  
Lin Zhao ◽  
Jianbo Wu ◽  
Lianzhen Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Neural Network Pid

Simulation Study of Vehicle Speed Variation Based on BP Neural Network PID Control

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yu Xu ◽  
Xuan Zheng ◽  
Shuai Fu ◽  
Zhiheng Wang ◽  
Haoxue Liu
Keyword(s):  
Neural Network ◽  
Simulation Study ◽  
Bp Neural Network ◽  
Pid Control ◽  
Vehicle Speed ◽  
Speed Variation ◽  
Neural Network Pid

Study for PID Temperature Control of Plant Growth Cabinet Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 229-232
Author(s):  
Zhi Qi Liu ◽  
Li Guo Tian ◽  
Meng Li ◽  
Jiang Lin Wei ◽  
Gao Li Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Plant Growth ◽  
Bp Neural Network ◽  
Pid Control ◽  
Response Speed ◽  
Fast Response ◽  
Control Methods ◽  
Target Temperature ◽  
Growth Environment ◽  
Neural Network Pid

Environment for plant growth is difficult to establish precise mathematical model. The conventional control methods are difficult to be well controlled, and put forward a neural network PID control temperature on the growth environment of plant. In this paper, taking the lettuce as an example, using MATLAB to simulate the PID control and PID control of BP neural network, the results proved that PID control of BP neural network has small overshoot, fast response speed and good stability compared with the traditional PID control, and better controlled temperature changing with the target temperature.

Study on Improved Neural Network PID Control for Coal Mining Power Grid Arc Suppression Coil Compensating System

2010 ◽  
Vol 426-427 ◽  
pp. 427-431
Author(s):  
C.Y. Ma ◽  
C.L. Wang ◽  
J.H. Liu ◽  
X.B. Li ◽  
R. Liang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Pid Control ◽  
Model Building ◽  
Control Method ◽  
Control Effect ◽  
Loop Control ◽  
Arc Suppression Coil ◽  
Neural Network Pid

The paper analyzed arc suppression coil with magnetic bias compensating system with linear system rules. The nonlinear and time-variable performances are considered during model building process. In order to optimize control effect, the paper adopted improved BP neural network PID controller with closed loop control method. Improve BP neural network with the combination of the two strategies, adding momentum method and adaptive learning rate adjustment, can not only effectively suppress the network appearing local minimum but also good to shorten learning time and improve stability of the network furthermore. The results of simulation and experiments indicate that arc suppression coil based on improved neural network with PID control method can quickly and accurately control the compensating capacitive current to an expected value and it has strong robustness. The paper also provided core controller with software and hardware designing scheme based on STM32 microcontroller.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

2014 ◽  
Vol 1030-1032 ◽  
pp. 1574-1577
Author(s):  
Dao Kun Zhang ◽  
Rui Huo ◽  
Shu Ying Li ◽  
Xing Ke Cui ◽  
Cui Ping Liu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulated Data ◽  
Chatter Suppression ◽  
Cutting Chatter ◽  
Adaptive Ability ◽  
Neural Network Pid ◽  
Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

Sign in / Sign up

Export Citation Format

Share Document