Skip to main content
SpringerLink
Log in

Coordination control of greenhouse environmental factors

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO2 concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. G. Cruz, R. C.Miranda, J. J. G. Escalante, I. L. L. Cruz, A. L. Herrera, J. I. D. L. Rosa. Calibration of a greenhouse climate model using evolutionary algorithms. Biosystems Engineering, vol. 104, no. 1, pp. 135–142, 2009

    Article  Google Scholar 

  2. J. F. J. Max, W. J. Horst, U. N. Mutwiwa, H. J. Tantau. Effects of greenhouse cooling method on growth, fruit yield and quality of tomato (Solanum lycopersicum L.) in a tropical climate. Scientia Horticulturae, vol. 122, no. 2, pp. 179–186, 2009.

    Article  Google Scholar 

  3. F. Xu, J. L. Chen, L. B. Zhang, H. W. Zhan. Self-tuning fuzzy logic control of greenhouse temperature using real-coded genetic algorithm. In Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision, IEEE, Singapore, pp. 1–6, 2006.

    Chapter  Google Scholar 

  4. Y. X. Li, S. F. Du. Advances of intelligent control algorithm of greenhouse environment in China. Transactions of the Chinese Society of Agricultural Engineering, vol. 20, no. 2, pp. 267–272, 2004. (in Chinese)

    Google Scholar 

  5. M. Trigui, S. Barrington, L. Gauthier. SE — Structures and environment: A strategy for greenhouse climate control, Part I: Model development. Journal of Agricultural Engineering Research, vol. 78, no. 4, pp. 407–413, 2001.

    Article  Google Scholar 

  6. N. Sigrimis, R. E. King. Advances in greenhouse environment control. Computers and Electronics in Agriculture, vol. 26, no. 3, pp. 217–219, 2000.

    Article  Google Scholar 

  7. Y. Z. Liu, G. H. Teng, S. R. Liu. The problem of the control system for greenhouse climate. Chinese Agricultural Science Bulletin, vol. 23, no. 10, pp. 154–157, 2007. (in Chinese)

    Google Scholar 

  8. A. Setiawan, L. D. Albright, R. M. Phelan. Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control. Computers and Electronics in Agriculture, vol. 26, no. 3, pp. 283–302, 2000.

    Article  Google Scholar 

  9. J. B. Cunha, C. Couto, A. E. Ruano. Real-time parameter estimation of dynamic temperature models for greenhouse environmental control. Control Engineering Practice, vol.5, no. 10, pp. 1473–1481, 1997.

    Article  Google Scholar 

  10. Y. Yi, H. Shen, L. Guo. Statistic PID tracking control for non-Gaussian stochastic systems based on T-S fuzzy model. International Journal of Automation and Computing, vol.6, no. 1, pp. 81–87, 2009.

    Article  Google Scholar 

  11. G. D. Pasgianos, K. G. Arvanitis, P. Polycarpou, N. Sigrimis. A nonlinear feedback technique for greenhouse environmental control. Computers and Electronics in Agriculture, vol. 40, vol. 1–3, pp. 153–177, 2003.

    Article  Google Scholar 

  12. R. C. Miranda, J. E. V. Ramos, R. R. P. Vera, G. H. Ruiz. Fuzzy greenhouse climate control system based on a field programmable gate array. Biosystems Engineering, vol. 94, no. 2, pp. 165–177, 2006.

    Article  Google Scholar 

  13. M. Nachidi, A. Benzaouia, F. Tadeo. Temperature and humidity control in greenhouses using the Takagi-Sugeno fuzzy model. In Proceedings of IEEE International Conference on Control Applications, IEEE, Munich, Germany, pp. 2150–2154, 2006.

    Chapter  Google Scholar 

  14. X. Y. Luo, Z. H. Zhu, X. P. Guan. Adaptive fuzzy dynamic surface control for uncertain nonlinear systems. International Journal of Automation and Computing, vol. 6, no. 4, pp. 385–390, 2009.

    Article  Google Scholar 

  15. S. C. Tong, Y. M. Li. Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks. International Journal of Automation and Computing, vol. 6, no. 2, pp. 145–153, 2009.

    Article  Google Scholar 

  16. P. M. Ferela, A. E. Ruano. Choice of RBF model structure for predicting greenhouse inside air temperature. In Proceedings of the 15th Triennial World Congress of the International Federation of Automatic Control, Barcelona, Spain, 2002.

  17. P. Sandra. Nonlinear model predictive via feedback linearization of a greenhouse. In Proceedings of the 15th Triennial World Congress, Barcelona, Spain, 2002.

  18. F. Fourati, M. Chtourou. A greenhouse control with feed-forward and recurrent neural networks. Simulation Modeling Practice and Theory, vol. 15, no. 8, pp. 1016–1028, 2007.

    Article  Google Scholar 

  19. P. M. Ferreira, E. A. Fariab, A. E. Ruano. Neural network models in greenhouse air temperature prediction. Neurocomputing, vol. 43, no. 1–4, pp. 51–75, 2002.

    Article  MATH  Google Scholar 

  20. A. G. Barto. Reinforcement learning in the real world. In Proceedings of IEEE International Joint Conference on Neural Networks, vol. 3, pp. 25–29, 2004.

    Google Scholar 

  21. C. J. C. H. Watkins, P. Dayan. Technical notes: Q-learning. Machine Learning, vol. 8, no. 3–4, pp. 279–292, 1992.

    MATH  Google Scholar 

  22. O. Jangmin, J. Lee, J. W. Lee, B. T. Zhang. Adaptive stock trading with dynamic asset allocation using reinforcement learning. Information Sciences, vol. 176, no. 15, pp. 2121–2147, 2006.

    Article  MATH  Google Scholar 

  23. K. Macek, I. Petrovic, N. Peric. A reinforcement learning approach to obstacle avoidance of mobile robots. In Proceedings of the 7th International Workshop on Advanced Motion Control, IEEE, pp. 462–466, 2002.

  24. S. D. Whitehead, L. J. Lin. Reinforcement learning of non-Markov decision processes. Artificial Intelligent, vol. 73, no. 1–2, pp. 271–306, 1995.

    Article  Google Scholar 

  25. P. Juell, P. Paulson. Case-based systems. IEEE Intelligent Systems, vol. 18, no. 4, pp. 60–67, 2003.

    Article  Google Scholar 

  26. Z. W. Xu, Z. Z. Liang, Z. Q. Sheng. Extended object model for product configuration design. International Journal of Automation and Computing, vol. 7, no. 3, pp. 289–294, 2010.

    Article  Google Scholar 

  27. J. Tian, X. Chen, S. P. Dong. Multi-modal reasoning medical diagnosis system integrated with probabilistic reasoning. International Journal of Automation and Computing, vol. 2, no. 2, pp. 134–143, 2005.

    Article  Google Scholar 

  28. R. A. Brooks. Intelligence without representation. Artificial Intelligence Journal, vol. 47, no. 1–3, pp. 139–159, 1991.

    Article  Google Scholar 

  29. B. C. Kuo. Automatic Control System, 7th ed., New York, USA: Prentice-Hall, 1995.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, PRC

    Feng Chen

  2. School of Information Technology, Illinois State University, Normal, IL, 61790, USA

    Yong-Ning Tang

  3. School of Computer and Information Science, Hefei University of Technology, Hefei, 230066, PRC

    Ming-Yu Shen

Authors
  1. Feng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong-Ning Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Yu Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Chen.

Additional information

This work was supported by National Natural Science Foundation of China (No. 60775014).

Feng Chen received the B. Sc. and M. Sc. degrees in computer sciences from Hefei University of Technology, Anhui, PRC in 1986 and 1994, respectively, and the Ph.D. degree in signal and information processing from the University of Science and Technology of China, Anhui, PRC in 2000. He is currently an associate professor with the Department of Automation, University of Science and Technology of China.

His research interests include artificial intelligence, data fusion, and intelligent traffic system.

Yong-Ning Tang received the B. Sc. and M. Sc. degrees in electrical engineering from Hefei University of Technology, Anhui, PRC in 1991 and 1994, respectively, and the Ph. D. degree in computer science from DePaul University, USA in 2008. He has been an assistant professor with the School of Information Technology, Illinois State University, Normal, USA since 2007.

His research interests include network monitoring and measurement, fault diagnosis, network security, and machine learning.

Ming-Yu Shen received the M. Sc. and Ph.D. degrees in computer sciences from Hefei University of Technology, Anhui, PRC in 1990 and 2008, respectively. He is currently an associate professor with the School of Computer and Information Science, Hefei University of Technology.

His research interests include artificial intelligence, wireless sensor networks, and network security.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, F., Tang, YN. & Shen, MY. Coordination control of greenhouse environmental factors. Int. J. Autom. Comput. 8, 147–153 (2011). https://doi.org/10.1007/s11633-011-0567-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-011-0567-3

Keywords

Search

Navigation