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

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.

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System of Annealing Furnace Temperature Control Based on Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1044-1045.881 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1090 ◽

2014 ◽

Vol 599-601 ◽

pp. 1090-1093 ◽

Cited By ~ 1

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.

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Design of Control System of Eddy Current Retarder Based on BP Neural Network PID Controller

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.223 ◽

2014 ◽

Vol 494-495 ◽

pp. 223-228

Author(s):

Fu Jin ◽

Jian Jun Sun ◽

Hong Bin Yu

Keyword(s):

Neural Network ◽

Control System ◽

Eddy Current ◽

Bp Neural Network ◽

Pid Controller ◽

Learning Algorithm ◽

Rapid Response ◽

Learning Ability ◽

Eddy Current Retarder ◽

Neural Network Pid

A new kind of algorithm of controller for eddy current retarder is designed in this paper. The eddy current retarder control system with traditional PID controller can't achieve a perfect performance in the rapid response. Back propagation (BP) neural network is one of artificial neural networks which has a good learning ability with a simple and recurrent structure, so it is suitable for controlling complicated eddy current retarder system. This paper introduces the principle, characteristics and learning algorithm of the BP neural network and designs the control system of eddy current retarder based on BP neural network PID controller by combining BP neural network and traditional PID. Making use of MATLAB, simulate this new kind of controller for eddy current retarder in the rapid response. Simulation results show it can improve the dynamic response performance and enhance the static precision compared to the traditional PID controller.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.1903 ◽

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.

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A Study of PID Control System Based on BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.328-330.1908 ◽

2011 ◽

Vol 328-330 ◽

pp. 1908-1911

Author(s):

Wei Liu ◽

Jian Jun Cai ◽

Xi Pin Fan

Keyword(s):

Neural Network ◽

Control System ◽

Conjugate Gradient ◽

Bp Neural Network ◽

Pid Control ◽

Pid Controller ◽

Steepest Descent ◽

The Neural Network ◽

On Line ◽

New Type

To deal with the defects of the steepest descent in slowly converging and easily immerging in partialm in imum,this paper proposes a new type of PID control system based on the BP neural network, which is a combination of the neural network and the PID strategy. It has the merits of both neural network and PID controller. Moreover, Fletcher-Reeves conjugate gradient in controller can make the training of network faster and can eliminate the disadvantages of steepest descent in BP algorithm. The parameters of the neural network PID controller are modified on line by the improved conjugate gradient. The programming steps under MATLAB are finally described. Simulation result shows that the controller is effective.

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Paste-filling weighing control system optimization based on neural network

World Journal of Engineering ◽

10.1108/wje-10-2016-0108 ◽

2017 ◽

Vol 14 (2) ◽

pp. 155-158 ◽

Cited By ~ 3

Author(s):

Guimei Wang ◽

Yong Shuo Zhang ◽

Lijie Yang ◽

Shuai Zhang

Keyword(s):

Neural Network ◽

Control System ◽

Coal Mine ◽

Bp Neural Network ◽

Back Propagation ◽

System Optimization ◽

Filling Material ◽

Content Type ◽

Control Precision ◽

Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.779.226 ◽

2015 ◽

Vol 779 ◽

pp. 226-232 ◽

Cited By ~ 1

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.

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Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.2416 ◽

2013 ◽

Vol 291-294 ◽

pp. 2416-2423 ◽

Cited By ~ 2

Author(s):

Guo Duo Zhang ◽

Xu Hong Yang ◽

Dong Qing Lu ◽

Yong Xiao Liu

Keyword(s):

Neural Network ◽

Control System ◽

Bp Neural Network ◽

Pid Controller ◽

Nuclear Reactor ◽

Control Method ◽

Setting Time ◽

Pressure Control ◽

Network Control ◽

Pressure System

The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.718-720.1682 ◽

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 .

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