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
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.