WCSE 2017
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.207

Study on Temperature Control in Plant Factory Based on Neural Network PID Controller

Shigang Cui, Jianhua Qin, Kun Liu

Abstract— In view of the characteristics of temperature in Plant Factory, such as nonlinearity, large delay, large delay, large inertia and so on, when the traditional strategy of PID control is used to control temperature, the effect of control is not ideal. Therefore, the paper combines the intelligent algorithm of Back Propagation (BP) neural network with the theory of traditional PID control, proposing the PID controller based on BP neural network self-tuning, and giving the corresponding algorithm and MATLAB simulation. Compared with the traditional PID control, the simulation results show that the BP neural network PID controller designed in this paper has better stability and robustness to control the temperature, and the quality of control has obvious advantages.

Index Terms— Plant Factory, temperature, BP neural network PID, MATLAB simulation

Shigang Cui, Jianhua Qin, Kun Liu
Tianjin University of Technology and Education, CHINA
Tianjin Key Laboratory of Information Sensing & Intelligent Control, CHINA

ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.17Xsrc="http://www.wcse.org/uploadfile/2019/0823/20190823055609629.png" style="width: 120px; height: 68px;" />[Download]

Cite: Shigang Cui, Jianhua Qin, Kun Liu, "Study on Temperature Control in Plant Factory Based on Neural Network PID Controller," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 1187-1191, Beijing, 25-27 June, 2017.