Res. Agr. Eng., 2022, 68(1):9-17 | DOI: 10.17221/37/2021-RAE

Non-linear control model for use in greenhouse climate control systemsOriginal Paper

Jalal Javadi Moghaddam*, Ghasem Zarei, Davood Momeni, Hamideh Faridi
Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

Keywords: actuator; control; humidity; simulink; temperature

Published: January 15, 2022  Show citation

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Javadi Moghaddam J, Zarei G, Momeni D, Faridi H. Non-linear control model for use in greenhouse climate control systems. Res. Agr. Eng. 2022;68(1):9-17. doi: 10.17221/37/2021-RAE.
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    References

    1. Arvantis K.G., Paraskevopoulos P.N., Vernados A.A. (2000): Multirate adaptative temperature control of greenhouses. Computers and Electronics in Agriculture, 26: 303-320. Go to original source...
    2. Boughamsa M., Ramdani M. (2018): Adaptive fuzzy control strategy for greenhouse micro-climate. International Journal of Automation and Control, 12: 108-125. Go to original source...
    3. Chen L., Du S., He Y., Liang M., Xu D. (2018): Robust model predictive control for greenhouse temperature based on particle swarm optimization. Information Processing in Agriculture, 5: 329-338. Go to original source...
    4. Chen W.H., You F. (2019): Efficient Greenhouse temperature control with data-driven robust model predictive. In: Proceedings of American Control Conference (ACC), July 1-3, 2020, Denver, USA: 1986-1991. Go to original source...
    5. Coelho J.P., de Moura Oliveira P.B., Cunha J.B. (2005): Greenhouse air temperature predictive control using the particle swarm optimisation algorithm. Computers and Electronics in Agriculture, 49: 330-344. Go to original source...
    6. Ferreira P.M., Faria E.A., Ruano A.E. (2002): Neural network models in greenhouse air temperature prediction. Neurocomputing, 43: 51-75. Go to original source...
    7. Gaudin V.C. (1981): Simulation and self-adaptive control of a greenhouse. [Ph.D. Thesis]. Nantes, University of Nantes.
    8. Ghoumari M.Y., Tantau H.J., Serrano J. (2005): Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control. Computers and Electronics in Agriculture, 49: 345-356. Go to original source...
    9. Gruber J.K., Guzmán J.L., Rodríguez F., Bordons C., Berenguel M., Sánchez J.A. (2011): Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation. Control Engineering Practice, 19: 354-366. Go to original source...
    10. Hamza A., Ramdani M. (2020): Non-PDC interval type-2 fuzzy model predictive microclimate control of a greenhouse. Journal of Control, Automation and Electrical Systems, 31: 62-72. Go to original source...
    11. Hanan J.J. (1998): Greenhouses: Advanced Technology for Protected Horticulture. New York, CRC Press: 708.
    12. Henten E.J. (1994): Greenhouse climate management: An optimal approach. [PhD Thesis]. Wageningen, Institute of Agriculture and Environmental Engineering. Go to original source...
    13. Kläring H.P., Hauschild C., Heißner A., Bar-Yosef B. (2007): Model-based control of CO2 concentration in greenhouses at ambient levels increases cucumber yield. Agricultural and Forest Meteorology, 143: 208-216. Go to original source...
    14. Körner O.S.G.V., Van Straten G. (2008): Decision support for dynamic greenhouse climate control strategies. Computers and Electronics in Agriculture, 60: 18-30. Go to original source...
    15. Lafont F., Balmat J.F. (2004): Fuzzy logic to the identification and the command of the multidimensional systems. International Journal of Computational Cognition, 2: 21-47.
    16. Lee C.C. (1990): Fuzzy logic in control systems: Fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20: 404-418. Go to original source...
    17. Lees M.J., Taylor J., Chotai A., Young P.C., Chalabi Z.S. (1996): Design and implementation of a proportional-integral-plus (PIP) control system for temperature, humidity and carbon dioxide in a glasshouse. Acta Horticulturae, 406: 115-124. Go to original source...
    18. Márquez-Vera M.A., Ramos-Fernández J.C., CereceroNatale L.F., Lafont F., Balmat J.F., Esparza-Villanueva J.I. (2016): Temperature control in a MISO greenhouse by inverting its fuzzy model. Computers and Electronics in Agriculture, 124: 168-174. Go to original source...
    19. Oueslati L. (1990): Multivariable control of an agricultural greenhouse by minimizing a quadratic criterion. [Ph.D. Thesis]. Toulon, University of Toulon.
    20. Pasgianos G.D., Arvanitis K.G., Polycarpou P., Sigrimis N. (2003): A nonlinear feedback technique for greenhouse environmental control. Computers and Electronics in Agriculture, 40: 153-177. Go to original source...
    21. Ramdani M., Hamza A., Boughamsa M. (2015): Multiscale fuzzy model-based short term predictive control of greenhouse microclimate. In: Proceedings of the IEEE 13th International Conference on Industrial Informatics (INDIN), 22-24 July, 2015, Cambdridge, UK: 1348-1353. Go to original source...
    22. Ramírez-Arias A., Rodríguez F., Guzmán J.L., Arahal M.R., Berenguel M., López J.C. (2005): Improving efficiency of greenhouse heating systems using model predictive control. IFAC Proceedings Volumes, 38: 40-45. Go to original source...
    23. Revathi S., Sivakumaran N. (2016): Fuzzy based temperature control of greenhouse. IFAC-PapersOnLine, 49: 549-554. Go to original source...
    24. Serale G., Fiorentini M., Capozzoli A., Bernardini D., Bemporad A. (2018): Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, 11: 631. Go to original source...
    25. Setiawan A., Albright L.D., Phelan R.M. (2000): Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control. Computers and Electronics in Agriculture, 26: 283-302. Go to original source...
    26. Sigrimis N., Antsaklis P., Groumpos P. (2001): Control advances in agriculture and the environment. IEEE Control System Magazine - Special Issue, 21: 8-12. Go to original source...
    27. Sigrimis N., Arvanitis K.G., Kookos I.K., Paraskevopoulos P.N. (1999): H∞-PI controller tuning for greenhouse temperature control. IFAC Proceedings Volumes, 32: 5644-5649. Go to original source...
    28. Souissi M. (2002): Climate control and modeling of a greenhouse. [Ph.D. Thesis]. Tunis, University of Tunis.
    29. Tap F. (2000): Economics-based optimal control of greenhouse tomato crop production. [Ph.D. Thesis]. Wageningen, Institute of Agriculture and Environmental Engineering.
    30. Van Straten G., van Willigenburg G., van Henten E., van Ooteghem R. (2010): Optimal Control of Greenhouse Cultivation. New York, CRC Press: 326. Go to original source...
    31. Xia Y., Yang H., Shi P., Fu M. (2010): Constrained infinitehorizon model predictive control for fuzzy-discrete-time systems. IEEE Transactions on Fuzzy Systems, 18: 429-436. Go to original source...
    32. Zou Q., Ji J., Zhang S., Shi M., Luo Y. (2010): Model predictive control based on particle swarm optimization of greenhouse climate for saving energy consumption. In: Proceedings of World Automation Congress, September 19-23, 2010, Kobe, Japan: 123-128.

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