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.

scholarly journals Design of a New Catheter Operating System for the Surgical Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xu Ma ◽  
Jinpeng Zhou ◽  
Xu Zhang ◽  
Yang Qi ◽  
Xiaochen Huang
Keyword(s):  
Neural Network ◽  
Operating System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Radiation Injury ◽  
Control Parameters ◽  
Simulation Experiments ◽  
Neural Network Pid

In interventional surgery, the manual operation of the catheter is not accurate. It is necessary to operate the catheter skillfully and effectively to protect the surgeon from radiation injury. The purpose of this paper is to design a new robot catheter operating system, which can help surgeons to complete the operation with high mechanical precision. On the basis of the original mechanical structure—real catheter, the operation information of the main end operator is collected. After the information is collected, the control algorithm of the system is improved, and the BP neural network is combined with the traditional PID controller to adjust the PID control parameters more effectively and intelligently so that the motor can reflect the output of the controller better and faster. The feasibility and superiority of the BP neural network PID controller are verified by simulation experiments.

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.

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

The Simulation of Robot Control Based on CMAC Neural Network PID Control Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 659-663
Author(s):  
Jian Li Yu ◽  
Ya Zhou Di ◽  
Lei Yin
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Robot Control ◽  
Control Algorithm ◽  
External Disturbance ◽  
System Model ◽  
Robot System ◽  
System Parameters ◽  
Cmac Neural Network ◽  
Neural Network Pid

According to the problem of nonlinear and uncertainty in robot control, this paper proposes a PID control algorithm based on CMAC neural network model, for the elimination of the influence of uncertainty caused by robot system parameters and external disturbance. The simulation results show that this algorithm can effectively overcome the uncertainties and external disturbance of robot system model, this algorithm has good robustness and stability, its performance is superior to the traditional PID control algorithm.

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.

Fast Neural Network PID Control Algorithm Based on Multi-Step Predictive Index Function

1998 ◽  
Vol 31 (14) ◽  
pp. 167-168
Author(s):  
Jin Qibing ◽  
Wang Jianhui ◽  
Wang Yunhua ◽  
Gu Shusheng
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Predictive Index ◽  
Index Function ◽  
Neural Network Pid
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