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.

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.

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.

Predictive Control of the VAV System Based on Recurrent Wavelet Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 928-933
Author(s):  
Jie Jia Li ◽  
Ben Wang ◽  
Xiao Yan Guo ◽  
Lu Lu Sun
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Method ◽  
Time Lapse ◽  
Wavelet Neural Network ◽  
Control Precision ◽  
Air Volume ◽  
Air Supply ◽  
High Control

An air supply control method of VAV system based on BP neural network is proposed in this paper, which combines with the recurrent wavelet neural network model, predictive control and optimization of parameters. With the proposed method, the air volume of the VAV system can be controlled accurately even if the change of the air is nonlinear and time-lapse. Compared with tradition control method, it has the advantages of rapidly converging, high control precision, strong skills of learning and wide application prospect.

Design of PID Temperature Control System Based on BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 1244-1247
Author(s):  
Zhi Yong Meng ◽  
Guo Qing Yu ◽  
Rui Jin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Nonlinear Function ◽  
Temperature Control System ◽  
Bp Network ◽  
Pid Algorithm

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

VAV Air-Conditioning System Control Using Neural Network Based MPC

2013 ◽  
Vol 846-847 ◽  
pp. 365-368
Author(s):  
Xiao Long Jia ◽  
Min Zhao ◽  
Yan Xia Jiang
Keyword(s):  
Neural Network ◽  
Air Conditioning ◽  
Uncertain System ◽  
System Control ◽  
Control Effect ◽  
Large Delay ◽  
Air Conditioning System ◽  
Control Precision ◽  
High Control ◽  
Adaptive Ability

Air conditioning system is known as a multi-variable, large delay, nonlinear and uncertain system, and it is difficult to achieve the ideal performance by using the traditional PID control strategy. In order to improve the system performance and reduce energy consumption at the same time, a neural network based MPC controller is proposed in this paper. After establishing the predictive model by using neural networks based on experiment data, the online optimization programming method is utilized to produce the control sequence of the future. And only the first control law is applied to the system to improve the control effect. The simulation result at last shows that the proposed strategy has strong robustness and adaptive ability, high control precision, better and reliable control effect and other advantages.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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