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.

The Application Research of PLC Temperature Control System Based on Fuzzy Neural Network PID

2013 ◽  
Vol 340 ◽  
pp. 517-522
Author(s):  
Yong Wei Lu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Experimental Result ◽  
Temperature Control System ◽  
Pid Algorithm ◽  
Fuzzy Neural ◽  
Neural Network Pid

it is hard to establish the accurate model by using the traditional PID algorithm, and hard to adjust the system parameter in nonlinear system. In order to solve this problem, this paper proposed PID algorithm based on Fuzzy Neural Network. This algorithm combined PID algorithm, fuzzy control algorithm and neural network algorithm together, and formed one kind intelligent control algorithm. This paper designed and researched this algorithm, and applied in the PLC temperature control system. The experimental result indicated that the fuzzy neural network PID controller improved the controller quality, conquered some question such as variable parameter and nonlinear, and enhanced the systems robustness.

Fuzzy Self-Tuning PID Temperature Control Modeling and Simulation System

2015 ◽  
Vol 713-715 ◽  
pp. 854-858 ◽  
Author(s):  
Zheng Qiang Guan ◽  
Xiao Ming Luo ◽  
Le Peng Song
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Pid Control ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Temperature Control System ◽  
Temperature Hysteresis ◽  
Fuzzy Pid Control ◽  
Time Problem ◽  
Self Tuning

For the current general heating control system tracks the set temperature hysteresis heating device to automatically adjust the real-time problem of poor design a fuzzy self-tuning proportional-integral-derivative (PID) controller parameters. Scoop out using PID control and fuzzy control algorithm combined method; fuzzy PID control parameters are adjusted. Use Matlab to model in simulink, the aryl the controller simulation analysis. The results show that the fuzzy self-tuning PID controller overshoot σ≈l%, steady-state error Island es=0. This method can improve the performance of the temperature control 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.

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.

scholarly journals PID Controller Based Nelder Mead Algorithm for Electric Furnace System with Disturbance

1970 ◽  
Vol 10 (1) ◽  
pp. 71-79
Author(s):  
Vunlop Sinlapakun ◽  
Wudhichai Assawinchaichote
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Electric Furnace ◽  
Pid Controller ◽  
Direct Synthesis ◽  
Optimization Procedure ◽  
Temperature Control System ◽  
Magnitude Response ◽  
Furnace Temperature ◽  
Traditional Methods

This paper presents a design of PID controller for furnace temperature control system with disturbance. Currently, PID controller has been used to operate in electric furnace temperature control system because its structure is simpler compared to others. However, the issue of tuning and designing PID controller adaptively and efficiently is still open. This paper presents an improved PID controller efficiency from tuning by Nelder Mead method. The parameters of PID controller shall be obtained from the Nelder Mead optimization procedure. Errors between desired magnitude response and actual magnitude response are calculated by using the Integral of Absolute Error (IAE). The proposed Nelder Mead based PID design method is simpler, more efficient and effective than the existing traditional methods included Ziegler Nichols, Cohen-Coon and Direct Synthesis. Simulation result shows that the performance of PID controller using this proposed method is better than traditional methods and resistant to disturbance.

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