Coordination control of greenhouse environmental factors

  • Feng ChenEmail author
  • Yong-Ning Tang
  • Ming-Yu Shen


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.


Q-learning case-based reasoning (CBR) greenhouse environmental factors coordination control coupled correlation trial-and-error 


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  1. [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, 2009CrossRefGoogle Scholar
  2. [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.CrossRefGoogle Scholar
  3. [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.CrossRefGoogle Scholar
  4. [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. [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.CrossRefGoogle Scholar
  6. [6]
    N. Sigrimis, R. E. King. Advances in greenhouse environment control. Computers and Electronics in Agriculture, vol. 26, no. 3, pp. 217–219, 2000.CrossRefGoogle Scholar
  7. [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. [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.CrossRefGoogle Scholar
  9. [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.CrossRefGoogle Scholar
  10. [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.CrossRefGoogle Scholar
  11. [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.CrossRefGoogle Scholar
  12. [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.CrossRefGoogle Scholar
  13. [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.CrossRefGoogle Scholar
  14. [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.CrossRefGoogle Scholar
  15. [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.CrossRefGoogle Scholar
  16. [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.Google Scholar
  17. [17]
    P. Sandra. Nonlinear model predictive via feedback linearization of a greenhouse. In Proceedings of the 15th Triennial World Congress, Barcelona, Spain, 2002.Google Scholar
  18. [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.CrossRefGoogle Scholar
  19. [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.zbMATHCrossRefGoogle Scholar
  20. [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. [21]
    C. J. C. H. Watkins, P. Dayan. Technical notes: Q-learning. Machine Learning, vol. 8, no. 3–4, pp. 279–292, 1992.zbMATHGoogle Scholar
  22. [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.zbMATHCrossRefGoogle Scholar
  23. [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.Google Scholar
  24. [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.CrossRefGoogle Scholar
  25. [25]
    P. Juell, P. Paulson. Case-based systems. IEEE Intelligent Systems, vol. 18, no. 4, pp. 60–67, 2003.CrossRefGoogle Scholar
  26. [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.CrossRefGoogle Scholar
  27. [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.CrossRefGoogle Scholar
  28. [28]
    R. A. Brooks. Intelligence without representation. Artificial Intelligence Journal, vol. 47, no. 1–3, pp. 139–159, 1991.CrossRefGoogle Scholar
  29. [29]
    B. C. Kuo. Automatic Control System, 7th ed., New York, USA: Prentice-Hall, 1995.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiPRC
  2. 2.School of Information TechnologyIllinois State UniversityNormalUSA
  3. 3.School of Computer and Information ScienceHefei University of TechnologyHefeiPRC

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