Study of Neural Network PID Control in Variable-frequency Air-conditioning System

2007 ◽  
Author(s):  
Jiangjiang Wang ◽  
Chunfa Zhang ◽  
Youyin Jing ◽  
Dawei An
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Air Conditioning ◽  
Variable Frequency ◽  
Air Conditioning System ◽  
Neural Network Pid

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.

scholarly journals Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

2020 ◽  
Vol 19 (2) ◽  
pp. 586
Author(s):  
Somaye A. Mohamadi ◽  
Abdulraheem J. Ahmed
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Environmental Conditions ◽  
Air Conditioning ◽  
Fuzzy Controller ◽  
Environmental Parameters ◽  
Thermal Conditions ◽  
Real Time Control ◽  
Air Conditioning System ◽  
Air Conditioning Systems

<span>Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.</span>

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