CONSTRAINED GA PI SL IDING MODE CONTROL O F INDOOR CLIMATE COU PLED MIMO GREENHOUSE MODEL

CONSTRAINED GA PI SL IDING MODE CONTROL O F INDOOR CLIMATE COU PLED MIMO GREENHOUSE MODEL

High accuracy trajectory trackingwith constrained inputs is challenging topic in greenhouse indoorenvironment control. This is due to nonlinearities and inputs/outputs coupling present in physical model ofgreenhouse. The objective of this study is about the problem of identification and control of a Multi Input MultiOutput (MIMO) greenhouse process. Proportional Integral (PI) and sliding mode controllers (SMC) are used inconjunction so that estimated outputs tracks desired trajectories with good performance as near as possible in spite ofcoupled dynamics and outside disturbances climate. To reflect the practical aspect, the constraints of the process andcontrol elements were taken into account .The results demonstrate the feasibility of PISMC control method.Simulation has been done i n the environment of Matlab and Simulink; it shows that combined controllers are capableto manage successfully the microclimate of greenhouse.

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  • [1] Revathi S, Sivakumaran N. Fuzzy Based Temperature Control of Greenhouse. IFAC PapersOnLine 2016; Vol 49, Issue 1, 549 554. https://doi.org/10.1016/j.ifacol.2016.03.112.
  • [2] Emelyanov S. V. Variable structure control systems. Nauka, Moscow; 1967.
  • [3] Blasco X, Martínez M, Herrero J M , Ramos C, Sanchis J. Model based predictive control of greenhouse climate for reducing energ y and water consumption. Comput Electron Agr 2007; 55:49 70. https://doi.org/10.1016/j. compag.2006.12.001.
  • [4] Draoui B, Bounaam a F, Boulard T, Bibi triki N. In situ Modelization of a Greenhouse Climate Including Sensible Heat, Water Vapor and CO2 Balances. EPJ Web of conferences 2013; 45.01023. https://doi.org/10.1051/epjconf/20134501023
  • [5] Manonmani A, Thyagarajan T, Elango M, Sutha S. Modelling and control of greenhouse system using neural networks. Transactions of the Institute of Measurement and Control 2016; Vol 4 0, Issue 3, 918 929 https://doi.org/10.1177/0142331216670235.
  • [6] Herrero J.M, Blasco X, Martínez M, Ramos C, Sanchis J. Non linear robust identification of a greenhouse model using multi objective evolutionary algorithms. Biosyst Eng 2007; Vol 98, Issue 3, 335 346. https://doi.org/10.1016/j.biosystemseng.2007.06.004.
  • [7] Patil S L, Tantau H.J, Salokhe V M. Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosyst Eng 2008; 99:423 431. https://doi.org/10.1016/j.biosystemseng.2007.11.009.
  • [8] Cheng M, Yuan H, Zhang M, Cheng M. The Research of Control Method of Greenhouse Based on Global Variable Prediction Model. Chem. Eng. Trans. 2016; Vol. 51,277 282. DOI: 10.3303/CET1651047.
  • [9] Trabe lsi A, Lafont F, Kamoun M, Enea G. Fuzzy identification of a greenhouse. Appl. Soft Comput. 2007 ; Vol 7, Issue 3, 1092 1101. https://doi.org/10.1016/j.asoc.2006.06.009.
  • [10] Draoui B. Caractérisation et analyse du comportement thermo hydrique d’une serre h orticole. Thèse de Doctorat de l’université de Nice Sophia Antipolis, France;1994.
  • [11] Boular d T, Draoui B, Neirac F. Calibration and validation of a greenhouse climate control model. Acta Hortic 1996; (ISHS), 406, 49 62. https://doi.org/10.17660/ActaHortic.1996.406.4
  • [12] Boulard T, Baille A . A simple greenhouse climate control model incorporating effects on ventilation and evaporative cooling. Agr Forest Meteorol 1993; 65, pp. 145 157. https ://doi.org/10.1016/0168 1923 (93)90001 X.
  • [13] Rodriguez F. Modeling and hierarchical control of greenhouse crop production (in Spanish). PhD thesis. University of Almeria, Spain; 2002.
  • [14] Boulard T, Wang S. Greenhouse crop transpiration simulation from external climate conditions. Agric For Meteorol. 2000; Vol 100, Issue 1, 25 34. https://doi.org/10.1016/0168 1923(93)90001 X
  • [15] Ghoumari M.E, Tantau H, S errano J. Non linear constrained mpc: real time implementation of greenhouse a ir temperature control. Comput Electron Agr 2005; 49, 345 356. https://doi.org/10.1016/j.compag.2005.08.005.
  • [16] Moughli H. Elaboration d’un modèle réduit d’ordre deux du bilan d’énergie d’une serre, Identification avec Optimisation des paramètres. Mémoir e de magister en physique énergétique;2007.
  • [17] Bounaama F, Draoui B. Greenhouse environmental control using optimized MIMO PID technique. Sens Transduc 2011; 133:44 52.
  • [18] HollandJ.H. Adaptation in Natural and Artificial System. Ann Arbor, the Univers ity of Michigan Press; 1975.
  • [19] Salami M, Cain G. An Adaptive PID Controller Based on Genetic Algorithm Processor. Genetic Algorithms in Engineering Systems: Innovations and Applications 1995; 12 14 September, Conference Publication No. 414, IEE. https://doi.org/10.1049/cp:19951030.
  • [20] Yusuf L.A, Magaji N. GA PID Controller for Position Control of Inverted Pendulum. Adaptive Science & Technology (ICAST), IEEE 6th International Conference on 2 9 31 Oct. 2014. https://doi.org/10.1109/ICASTECH.2014.7068099.
  • [21] Lammari K, Bouaama F, Dra oui B, Mrah B, HaidasM. GA Optimization of the Coupled Climate model of an order two of a Greenhouse. Energy Procedia 2012; 18, 416 425. https://doi.org/10.1016/j. egypro.2012.05.053.
  • [22] Dai C, Yao M, Xie Z, Chen C, Liu J. Parameter optimization for growth model of greenhouse crop using genetic algorithms. Appl Soft Comput 2009; 9:13 19. https://doi.org/10.1016/j.asoc.2008.02.002.
  • [23] Fourati F, Chtourou M. A gree nhouse control with feed forward and recurrent neural networks . Simulat. Model. Pract. Theor. 2007; Vol 15, Issue 8 , 1016 1028. https://doi.org/10.1016/j.simpat.2007.06.001.
  • [24] Taylor C.J, Leigh P, Price L, Young P.C, Vranken E, Berckmans D. Proportional integral plus (PIP) control of ventilation rate in agricultural buildings. Control Eng Pract 2004; 12, 225 233. https://doi.org/10.1016/S0967 0661(03)00060 1.
  • [25] Slotine J.J.E, Li W, Applied Nonlinear Control. Prentice Hall; 1991.
  • [26] Bandy opadhyay B, Deepak F,KimK.S. Sliding Mode Control Using Novel Sliding Surfaces. Springer; 2009.
  • [27] Utkin V.I. Variable Structure Systems with Sliding Modes. IEEE Trans. Autom. Control. 1977; 22, 2, 212 222. http://dx.doi.org/10.1109/tac.1977.1101446.