A Variable Speed Control Hydraulic System Based on BP Neural Network PID Controller

2011 ◽  
Vol 101-102 ◽  
pp. 439-442
Author(s):  
Fang Ping Huang ◽  
Tian Hao Peng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Pid Controller ◽  
Good Control ◽  
Hydraulic Systems ◽  
Variable Speed ◽  
Control Effect ◽  
Research Results ◽  
Neural Network Pid

The variable speed hydraulic systems have many advantages, and research about this field in recent years has developed rapidly. In this paper, a variable speed hydraulic system is studied using BP Neural Network PID controller. The research results show that using BP Neural Network PID controller can achieve good control effect.

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

2015 ◽  
Vol 779 ◽  
pp. 226-232 ◽  
Author(s):  
Shi Xing Zhu ◽  
Yue Han ◽  
Bo Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Experimental Testing ◽  
Computation Time ◽  
Landing Gear ◽  
Time Varying ◽  
Control Effect ◽  
Simulation And Experiment ◽  
Neural Network Pid

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

Based on the BP Neural Network-PID Series Control Boiler Main-Steam Temperature System Research

2011 ◽  
Vol 354-355 ◽  
pp. 968-973 ◽  
Author(s):  
Wen Zhu ◽  
Jian Ping Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Cascade Control ◽  
Steam Temperature ◽  
Control Effect ◽  
Real Time System ◽  
Main Steam ◽  
Neural Network Pid

Due to the boiler main-steam temperature system exists more serious characteristics,such as much capacitive,nonlinear,time-varying and lag, so adopt cascade control strategy. This paper design a control algorithm which is based on BP neural network, it can accelerate the regulating time, and combined with the conventional PID controller, constitute the BP neural network - PID cascade control strategy. This control strategy not only contain the BP neural network control in real time system strong anti-interference ability characteristic, but also fully utilize the PID controller response speed characteristic. The simulation results show that based on the BP neural network - PID series control boiler main-steam temperature system can achieve satisfactory control effect.

Application of PID Control Algorithm Based on Improved BP Neural Network to Turntable System

2012 ◽  
Vol 490-495 ◽  
pp. 191-194
Author(s):  
Yang Feng ◽  
Qing Jiu Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Good Control ◽  
Least Square ◽  
Tracking Accuracy ◽  
Control Effect ◽  
Bp Network

Aiming at the problem that traditional PID control algorithm is difficult to get ideal control effect, a PID control algorithm based on improved BP neural network is proposed to improve the performance of turntable system. According to the structure and characteristic of BP neural network, the construction of PID controller and the description of improved BP neural network algorithm are introduced at first. Then, on the basis of the least square method and neural network prediction model of controlled object, the weight adjustment algorithm of PID is improved by replacing the measured values of BP network with calculated forecast output. A mathematical model of turntable control system is established and simulated. Simulation results show that the improved BP neural network PID controller has good control performance, high tracking accuracy and strong system robustness, which can be better applied to turntable system.

Research on Yaw Stability Control of Active Front Steering Based on BP Neural Network PID

2013 ◽  
Vol 765-767 ◽  
pp. 1903-1907
Author(s):  
Jie Wei ◽  
Guo Biao Shi ◽  
Yi Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Steering System ◽  
Stability Control ◽  
Steering Angle ◽  
System A ◽  
Active Front Steering ◽  
Yaw Stability ◽  
Neural Network Pid

This paper proposes using BP neural network PID to improve the yaw stability of the vehicle with active front steering system. A dynamic model of vehicle with active front steering is built firstly, and then the BP neural network PID controller is designed in detail. The controller generates the suitable steering angle so that the vehicle follows the target value of the yaw rate. The simulation at different conditions is carried out based on the fore established model. The simulation results show the BP neural network PID controller can improve the vehicles yaw stability effectively.

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.

Path planning and tracking for vehicle parallel parking based on preview BP neural network PID controller

2015 ◽  
Vol 21 (3) ◽  
pp. 199-208
Author(s):  
Xuewu Ji ◽  
Jian Wang ◽  
Youqun Zhao ◽  
Yahui Liu ◽  
Liguo Zang ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Parallel Parking ◽  
Neural Network Pid

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

2014 ◽  
Vol 599-601 ◽  
pp. 1090-1093 ◽  
Author(s):  
Chun Hua Li ◽  
Shao Xiong Xu ◽  
Yang Xie ◽  
Jie Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Speed Control ◽  
Regulation System ◽  
Particle Swarm Algorithm ◽  
Variable Frequency ◽  
Speed Control System ◽  
Neural Network Pid

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

scholarly journals Based on Neural Network PID Controller Design and Simulation

2013 ◽  
Vol 756-759 ◽  
pp. 514-517
Author(s):  
Hao Xu ◽  
Jin Gang Lai ◽  
Zhen Hong Yu ◽  
Jiao Yu Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
Industrial Process ◽  
Temperature Control System ◽  
The Neural Network ◽  
Mathematical Problems ◽  
Pid Controller Design ◽  
Neural Network Pid

The technologic of PID control is very conventional. There is an extensive application in many fields at present. The PID controller is simple in structure, strong in robustness, and can be understood easily. Then neural networks have great capability in solving complex mathematical problems since they have been proven to approximate any continuous function as accurately as possible. Hence, it has received considerable attention in the field of process control. Due to the complication of modern industrial process and the increase of nonlinearity, time-varying and uncertainty of the practical production processes, the conventional PID controller can no longer meet our requirement. This paper introduces the theoretical foundation of the BP neural network and studying algorithm of the neural network briefly, and designs the PID temperature control system and simulation model based on BP neural network.

scholarly journals Development of a Robotic Catheter Manipulation System Based on BP Neural Network PID Controller

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xu Ma ◽  
Jinpeng Zhou ◽  
Xu Zhang ◽  
Qi Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Interventional Therapy ◽  
Remote Operation ◽  
Manipulation System ◽  
Neural Network Pid ◽  
Strong Radiation ◽  
Synchronization Performance ◽  
Better Than

In the process of artificial interventional therapy, the operation of artificial catheter is not accurate, which will bring strong radiation damage to surgeons. The purpose of this study is to develop a catheter operating system of surgical robot to assist doctors in remote operation and avoid the influence of radiation. BP neural network plays an important role in the flexibility and rapidity of control. According to the actual output of the system, the control parameters of the controller are constantly adjusted to achieve better output effect. This paper introduces the practical application of BP neural network PID controller in the remote operation of the system and compares with the traditional PID controller. The results show that the new control algorithm is feasible and effective. The results show that the synchronization performance of BP neural network PID controller is better than that of traditional PID controller.

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.

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