|Table of Contents|

Temperature and humidity control for environmental test chamber based on genetic algorithmoptimized parameters of PID controller(PDF)


Research Field:
Publishing date:


Temperature and humidity control for environmental test chamber based on genetic algorithmoptimized parameters of PID controller
Li ShujiangZhao ChenSu XihuiWang Xiangdong
School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,China
environmental test chamber temperature and humidity control genetic algorithm proportion integral differential controller decoupling
The control system of temperature and humidity in the big volume environmental test chamber has the characteristics of nonlinearity,time-varying and coupling.The traditional parameter tuning method of proportion integral differential(PID)can not meet the requirements of the temperature and humidity control in the environmental test chamber.The intelligent control of the temperature and humidity of the environmental test chamber can be realized only by obtaining the optimal parameters of the PID controller.This paper proposes a control algorithm based on genetic algorithm(GA)optimized parameters of PID controller,which is named as GA-PID.Firstly,the temperature and humidity is decoupled by the predictive decoupling method.The objective function is used as the evaluation value of the controller,and the optimal solution of PID control parameters can be obtained through the selection,crossover and mutation of genetic algorithm.The proposed control algorithm can make up the deficiency of conventional PID in temperature and humidity control system.Simulation experiment is carried out by MATLAB.The results show that the temperature and humidity can be effectively decoupled,the GA-PID control algorithm can achieve a more rapid,accurate and stable temperature and humidity control.The proposed control system has better control performance.


[1] Kumar P,Skouloudis A N,Bell M,et al.Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings[J].Science of the Total Environment,2016,560:150-159.
Chi Dong,Li Liqing,Ma Weiwu.Experience research on formaldehyde emission from wood-based panels and measurement of emission parameters[J].China Environmental Science,2014,32(2):532-538.
Zhou Lili,Chen Huiming,Song Naining,et al.Development and performance evaluation of an environmental testing combination of chamber[J].Chinese Journal of Environmental Engineering,2013,7(9):3525-3530.
[4]Xie H,Kim C N.Numerical modeling of VOCs emission from a multi-layer carpet[J].Heat and Mass Transfer,2013,49(7):1009-1019.
[5]Parthasarathy S.Modeling indoor exposures to VOCs and SVOCs as ventilation rates vary[C]//Healthy Buildings 2012—10th International Conference.Santa Cruz,USA:International Society of Indoor Air Quality and Climate,2012:2497-2502.
Wang Lixin,Cui Heng,Yang Zhihuan,et al.The study of smiulating p-dichlorobenzene emission and effect factors using environmental test chamber[J].Acta Scientiae Cricum Stantiae,2007,27(1):45-52.
[7]Manoukian A,Buiron D,Temime-Roussel B,et al.Measurements of VOC/SVOC emission factors from burning incenses in an environmental test chamber:influence of temperature,relative humidity,and air exchange rate[J].Environmental Science & Pollution Research,2016,23(7):6300-6311.
[8]Mahyuddin N,Awbi H B,Essah E A.Computational fluid dynamics modelling of the air movement in an environmental test chamber with a respiring manikin[J].Journal of Building Performance Simulation,2015,8(5):359-374.
Zhang Fuhua,Zeng Dongdong,Sun Keliang.Study of the control algorithm of temperature and humidity for large scale environmental test chamber[J].Wood Processing Machinery,2014(5):26-28.
Li Ke,Pang Liping,Liu Wangkai,et al.System model simulation and control method used in environmental simulation chambers[J].Journal of Beijing University of Aeronautics and Astronautics,2007,33(5):535-538.
[11]Hu M Y,Fang K L.The temperature and humidity control of artificial climate chamber based on feed-forward compensation decoupling[C]//Proceedings of the 4th International Conference on Manufacturing Science and Technology.Zurich-Durnten,Switzerland:Trans Tech Publications,2013,816:343-347.
Zhang Tianpeng,Si Hui,Yan Xiaolin,et al.Improved plan for a constant temperature and humidity environmental chamber system for furniture detection[J].Forestry Machinery & Woodworking Equipment,2015(4):13-16.
[13]Xie H,Song K,Yang S,et al.On decoupling control of the VGT-EGR system in diesel engines:a new framework[J].IEEE Transactions on Control Systems Technology,2016,24(5):1977-1796.
Li Shuying,Pan Ya,Fei Wei,et al.Virtual machine placement method based on grouping genetic algorithm[J].Journal of Nanjing University of Science and Technology,2016,40(3):322-327.
[15]Ar Y,Bostanci E.A genetic algorithm solution to the collaborative filtering problem[J].Expert Systems with Applications,2016,61:122-128.
Zhao Xiaoqiang,He Hao.Improved DRSGA for flexible job shop scheduling[J].Journal of Nanjing University of Science and Technology,2016,40(3):297-302.
Tian Zhongda,Gao Xianwen,Li Shujiang,et al.Prediction method for network traffic based on genetic algorithm optimized echo state network[J].Journal of Computer Research and Development,2015,52(5):1137-1145.
[18]Holland J H.Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence[M].Cambridge,Massachusetts,USA:MIT Press,1992.
Wang Zan,Fan Xiangyu,Zhou Yuguo,et al.Genetic algorithm based multiple faults localization technique[J].Journal of Software,2016,27(4):879-890.
[20]Agapie A,Wright A H.Theoretical analysis of steady state genetic algorithms[J].Applications of Mathematics,2014,59(5):509-525.
[21]Morales-Reyes A,Escalante H J,Letras M,et al.An empirical analysis on dimensionality in cellular genetic algorithms[C]//Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation.New York,USA:ACM,2015:895-902.
Duan Moyi.Study of network invulnerability and evaluation index[J].Journal of Chinese Computer Systems,2013,34(11):2553-2557.
[23]Tian Z D,Wang Y H,Li S J.T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm[J].International Journal of Modelling,Identification and Control,2016,25(4):323-334.
Li Jiwen,Luo Deyuan,Liu Rong.Application of digital PID in the greenhouse environment control system[J].Journal of Agricultural Mechanization Research,2009,31(1):205-207.


Last Update: 2017-08-31