Application of RBF Neural Network PID Controller in the Rectification Column Temperature Control System

2013 ◽  
Author(s):  
Yan Zhang ◽  
Chao Yingliu ◽  
Xueling Song ◽  
Zhifei Yan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
Temperature Control System ◽  
Rectification Column ◽  
Column Temperature ◽  
Neural Network Pid

Research on Temperature Control System for Vacuum Annealing Furnace Based on Neural Network-PID

2014 ◽  
Vol 644-650 ◽  
pp. 298-304
Author(s):  
Nai Lu Zhang ◽  
Li He ◽  
Wei Huang ◽  
Xin Liu ◽  
Li Bo Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Annealing Temperature ◽  
Vacuum Annealing ◽  
Rare Metal ◽  
Temperature Control System ◽  
Annealing Furnace ◽  
Neural Network Pid

Vacuum annealing is an important part in rare metal tube production, accurate control of the annea ling temperature has enormous influence on the quality of tubes.According to the technological characteristics and temperature control requirements of the vacuum annealing furnace ,a high precision temperature control system was built based on IPC, intelligent temperature controller and thyratron transistor power-regulator.The neural network-PID strategy was proposed to control temperature online through OPC interface, which realized accurate control and automatic detection of the whole process of annealing temperature. Field data indicates that this system has realized the accurate control of the vacuum annealing temperature, effectively improves the quality of rare metal tubes and has extensive application value.

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.

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.

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.

Effect and Simulation on Control System of Boiler Parameters

2013 ◽  
Vol 411-414 ◽  
pp. 1711-1715
Author(s):  
Bing Hua Jiang ◽  
Li Fang ◽  
Hang Biao Guo
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Temperature Control ◽  
Pid Controller ◽  
Static Stability ◽  
Temperature Control System ◽  
Integrated Process ◽  
And Control

In this paper, taking integrated process and control platform as the background , did the research on mathematical model of boiler liner and parameters on the performance of the control system. First, created a mathematical model of the temperature of the boiler liner. Second, selected the PID controller to control the temperature control system in the case of the PID controller parameters remained unchanged. Finally, changed the boiler parameters, analyzed and compared the simulation waveforms of different boiler parameters in order to get the conclusion that different parameters had different influence on the static stability of the temperature control system and the temperature control system had anti-jamming capability.

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