IJAT Vol.15 No.2 pp. 243-248
doi: 10.20965/ijat.2021.p0243

Technical Paper:

Design of an Intelligent Greenhouse Remote Control System Based on a Fuzzy Neural Network

Yajuan Jia

Shanxi Professional College of Finance
No.10, South Lane, Yingxin Street, Taiyuan City, Shanxi 030008, China

Corresponding author

September 24, 2020
December 14, 2020
March 5, 2021
fuzzy neural network, greenhouse, remote control, temperature control

There are many changing factors in a greenhouse, and the traditional control method has been unable to obtain a good control effect. In this study, focusing on the fuzzy neural network (FNN), the principles of two control methods and the advantages of their combination were analyzed, an intelligent remote control system for a greenhouse based on the FNN that controls the temperature and humidity was designed, and a simulation experiment was performed in the Simulink environment. The results demonstrated that compared with the traditional proportion, integration, differentiation (PID) control system and the genetic algorithm + fuzzy PID control system, the FNN-based system designed in this study achieved better performance in temperature and humidity control. The temperature error of the FNN-based system was smaller than 1C, the humidity error was approximately 2%, and the change in the error values was stable. The experimental results verify the reliability of the FNN and provide some theoretical basis for the intelligent control of greenhouses.

Cite this article as:
Y. Jia, “Design of an Intelligent Greenhouse Remote Control System Based on a Fuzzy Neural Network,” Int. J. Automation Technol., Vol.15 No.2, pp. 243-248, 2021.
Data files:
  1. [1] W. Gong, X. Zhang, Y. Wang, W. Tang, Y. Chen, and D. Li, “Review of Intelligent Control Methods for Greenhouse Cluster Systems,” 2019 Int. Conf. on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), USA, pp. 235-239, October 2019.
  2. [2] S. R. Ramin, J. W. Jones, K. R. Thorp, D. Ahmad, M. H. Che, and S. Taheri, “Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: a review,” Int. Agrophys., Vol.32, pp. 287-302, 2018.
  3. [3] B. Mohammadi, S. F. Ranjbar, and Y. Ajabshirchi, “Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse,” Inform. Process. Agric., S2214317317302093, 2018.
  4. [4] M. Baek, M. Lee, H. G. Kim, J. Park, Y. Cho, and C. Shin, “A study on Greenhouse Management Framework for intelligent control service of greenhouse,” Int. J. Smart Home, Vol.10, pp. 129-138, 2016.
  5. [5] Y. Jin, “Applications of Fuzzy Control in Greenhouse Intelligent Control System,” Proc. of Int. Conf. Information Sciences, pp. 755-758, 2015.
  6. [6] J. Gu, M. Yin, and L. Wang, “Research on greenhouse intelligent control system based on genetic optimization fuzzy PID Algorithm,” Rev. Fac. Ing., Vol.32, pp. 382-388, 2017.
  7. [7] Z. Faisal, F. Ali, and A. Abbas, “An Intelligent Temperature Control System for a Prototype Greenhouse,” Int. J. Comput. Appl., Vol.178, pp. 28-35, 2017.
  8. [8] S. Revathi, T. K. Radhakrishnan, and N. Sivakumaran, “Climate control in greenhouse using intelligent control algorithms,” Proc. of 2017 American Control Conf. (ACC), pp. 887-892, 2017.
  9. [9] L. Qin, L. Lu, C. Shi, G. Wu, and Y. Wang, “Implementation of IOT-based Greenhouse Intelligent Monitoring System,” Trans. Chin. Soc. Agric. Mach., Vol.46, pp. 261-267, 2015.
  10. [10] G. E. Narasimhan and J. Bettyjane, “Implementation and study of a novel approach to control adaptive cooperative robot using fuzzy rules,” Int. J. Inform. Tech., pp. 1-8, 2020.
  11. [11] M. M. Zirkohi and T. C. Lin, “Interval type-2 fuzzy-neural network indirect adaptive sliding mode control for an active suspension system,” Nonlinear Dynam., Vol.79, pp. 513-526, 2015.
  12. [12] H. Acikgoz, C. Yildiz, R. Coteli, and B. Dandil, “DC-link voltage control of three-phase PWM rectifier by using artificial bee colony based type-2 fuzzy neural network,” Microprocess. Microsy., Vol.78, 103250, 2020.
  13. [13] Y. Dong and H. Wang, “Robust output feedback stabilization for uncertain discrete-time stochastic neural networks with time-varying delay,” Neural Process. Lett., Vol.51, No.1, pp. 83-103, 2020.
  14. [14] R. Wang and B. Zha, “A Research on the Optimal Design of BP Neural Network Based on Improved GEP,” Int. J. Pattern Recogn., Vol.33, pp. 1959007.1-1959007.14, 2019.
  15. [15] L. A. Zadeh, “Fuzzy sets,” Inform Control, Vol.8, No.3, pp. 338-353, 1965.
  16. [16] M. Mansouri, M. Teshnehlab, and S. M. Aliyari, “Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems,” ISA Trans., Vol.56, pp. 28-41, 2015.
  17. [17] X. Hu, B. Xu, and C. Hu, “Robust Adaptive Fuzzy Control for HFV With Parameter Uncertainty and Unmodeled Dynamics,” IEEE T. Ind. Electron., p. 1, 2018.
  18. [18] F. Gouadria, L. Sbita, and N. Sigrimis, “Comparison between self-tuning fuzzy PID and classic PID controllers for greenhouse system,” Proc. of 2017 Int. Conf. on Green Energy Conversion Systems (GECS), pp. 1-6, 2017.
  19. [19] N. He, G. Yi, and S. Liang, “Tracking time-discrete quasi-sliding mode variable structure controller for temperature and humidity of greenhouse,” Proc. of 2015 IEEE Int. Conf. on Mechatronics and Automation (ICMA), pp. 2273-2278, 2015.
  20. [20] X. Wang and H. Yu, “Research on Control System of Intelligent Greenhouse of IoT Based on Zigbee,” J. Phys. Conf. Ser., Vol.1345, 042036, 2019.

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