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Enhancement in Smart Operation of Greenhouse Environment Using Intelligent Biomimetic Control Framework

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Abstract

The greenhouse is a complex nonlinear system, which provides a suitable growing environment for plants. Realizing a comprehensive control for such systems is more challenging in the premise of the presence of model parameter uncertainties and variable time delay. This work presents, develops, and tests the methodology entrenched from the computational inspiration of the biologic immune concept. Mostly, in contention to the widely used classical approach of greenhouse control, an essential facet of the bioinspired algorithm is integrated that synergizes its strength. In this work, the study considers proportional integral and pseudo-derivative-feedback controllers and the artificial immune feature supplements it for the proposal of a new unifying control framework. The non-intelligent techniques are more classical, whereas intelligent techniques involving resistant feature is still relatively new to the control arena. The main distinguishing feature is the superficial resemblance to the conventional controller, but it is inherently more flexible and sophisticated and can exploit the power of heuristics. To deal with the system realistically and to ensure robustness, the Kharitonov theorem is used to synthesize the controller by elucidating the relationship between coefficient perturbations, set of test eight specially constructed vortex polynomials and its stability. Further, Hardware in Loop simulation is performed to verify the performance near physical operating conditions and the results validate the efficacy of the proposed controller frameworks with improvement in dynamic performance and robustness.

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References

  1. Albright LD, Gates RS, Arvanitis KG, Drysdal AE. Environmental control for plants on earth and in space. IEEE Control Syst Mag. 2001;5(21):28–47.

    Google Scholar 

  2. Azuaje F. Artificial immune systems: a new computational intelligence approach. Neural Netw. 2003. https://doi.org/10.1016/S0893-6080(03)00058-3.

    Article  Google Scholar 

  3. Bennis N, Duplaix J, Enéa G, Haloua M, Youlal H. Greenhouse climate modelling and robust control. Comput Electron Agric. 2008;61(2):96–107.

    Article  Google Scholar 

  4. Van Beveren PJM, Bontsema J, van Straten G, van Henten EJ. Optimal control of greenhouse climate using minimal energy and grower defined bounds. Appl Energy. 2015;159:509–19.

    Article  ADS  Google Scholar 

  5. Blasco X, Martinez M, Herrero JM, Ramos C, Sanchis J. Model-based predictive control of greenhouse climate for reducing energy and water consumption. Comput Electron Agric. 2007;55(1):49–70.

    Article  Google Scholar 

  6. Bot GPA. Greenhouse climate control. In: Backer, Bot JC, Gpa A, Challa H, Van de Braak NJ, editors. Greenhouse climate control: an integrated approach. Wageningen: Wageningen Pers; 1995. p. 211–47.

    Chapter  Google Scholar 

  7. Castañeda Miranda R, Ventura Ramos E, del RocíoPeniche Vera R, Herrera-Ruiz G. Fuzzy greenhouse climate control system based on a field programmable gate array. Biosyst Eng. 2006;94(2):165–77.

    Article  Google Scholar 

  8. Chen L, Du S, Xu D, He Y, Liang M. Sliding mode control based on disturbance observer for greenhouse climate systems. Math Probl Eng. 2008;2018:1–8.

    Google Scholar 

  9. Fleming PJ, Purshouse RJ. Genetic algorithms in control systems engineering. IFAC Proc Vol. 1999;26(2):605–12.

    Article  Google Scholar 

  10. González-Vidal A, Mendoza-Bernal J, Ramallo AP, Zamora MÁ, Martínez V, Skarmeta AF. Smart operation of climatic systems in a greenhouse. Agriculture. 2022;12(10):1729.

    Article  Google Scholar 

  11. Gurban E H, Andreescu G D. Comparison study of PID controller tuning for greenhouse climate with feedback-feedforward linearization and decoupling. Proc. of 16th international conference on system theory, control and computing (ICSTCC). 2012; 1–6.

  12. Gustavo C, Marco H, Ramon J, Hanna A, Oscar C. A practical hybrid control approach for a greenhouse microclimate: a hardware-in-the-loop implementation. Agriculture. 2022;12(11):1916–1916.

    Article  Google Scholar 

  13. Herrero J M, Blasco X, Martinez M, Sanchis J (2008) Multiobjective tuning of robust PID controllers using evolutionary algorithms. Proc of conference on applications of evolutionary computing 515–524.

  14. Huang YJ, Wang YJ. Robust PID tuning strategy for uncertain plants based on the Kharitonov theorem. ISA Trans. 2000;39(4):419–31.

    Article  CAS  PubMed  Google Scholar 

  15. Jaen Cuellar AY, de Romero Troncoso RJ, Morales Velazquez L, Osornio-Rios RA. PID controller tuning optimization with genetic algorithms in servo systems. Inte J Adv Robot Syst. 2013;10(9):1–14.

    Google Scholar 

  16. Jerne NK. Towards a network theory of the immune system. Ann Immunol (Paris). 1974;125(1–2):373–89.

    CAS  Google Scholar 

  17. Kolokotsa D, Saridakis G, Dalamagkidis K, Dolianitis S, Kaliakatsos I. Development of an intelligent indoor environment and energy management system for greenhouses. Energy Conversat Manag. 2010;51(1):155–68.

    Article  CAS  Google Scholar 

  18. Küppers R (2010) Overview of the immune system. The lymphoid neoplasms.

  19. Lin G, Liu L (2010) Tuning PID controller using adaptive genetic algorithms. 2010 5th International conference on computer science and education 519–523.

  20. Boughamsa M, Ramdani M. Adaptive fuzzy control strategy for greenhouse micro-climate. Int J Autom Control. 2018;12(1):108–25.

    Article  Google Scholar 

  21. Occhipinti L, Nunnari G (1996) Synthesis of a greenhouse climate controller using Al-based techniques. Proc. IEEE Int. Conf. MELECON 230–233.

  22. Pasgianos GD, Syrcos G, Arvanitis KG, Sigrimis NA. Pseudo-derivative feedback-based identification of unstable processes with application to bioreactors. Comput Electron Agric. 2003;40(1–3):5–25.

    Article  Google Scholar 

  23. Phelan RM. Automatic control systems. Cornell University Press; 1997.

    Google Scholar 

  24. Preeth SKSL, Dhanalakshmi R, Kumar R. An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. J Ambient Intell Humaniz Comput. 2018. https://doi.org/10.1007/s12652-018-1154-z.

    Article  Google Scholar 

  25. Revathi S, Radhakrishnan TK, Sivakumaran N. Climate control in greenhouse using intelligent control algorithms. Am Control Conf. 2017;2017:887–92.

    Google Scholar 

  26. Seginer I, Boulard T, Bailey BJ. Neural network models of the greenhouse climate. J Agric Eng Res. 1994;59(3):203–16.

    Article  Google Scholar 

  27. Seshagiri RA, Chidambaram M. PI/PID controllers design for integrating and unstable systems. Adv Ind Control. 2012. https://doi.org/10.1007/978-1-4471-2425-2_3.

    Article  Google Scholar 

  28. Setiawan A, Albright LD, Phelan RM. Application of pseudo-derivative-feedback algorithm in greenhouse air temperature control. Comput Electron Agric. 2000;26(3):283–302.

    Article  Google Scholar 

  29. Sigrimis N, Arvanitis KG, Ferentinos KP, Anastasiou A. An intelligent noninteracting technique for climate control of greenhouses. IFAC Proc Vol. 2002;35(1):323–8.

    Article  Google Scholar 

  30. Stanghellini C, Van Meurs WT. Environmental control of greenhouse crop transpiration. J Agric Eng Res. 1992;51:297–311.

    Article  Google Scholar 

  31. Tantau H J (1985) Greenhouse climate control using mathematical models. Acta Hortic 449–460.

  32. Karanisa T, Achour Y, Ouammi A, Sayadi S. Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar. Environ Syst Decis. 2022;42(4):521–46.

    Article  Google Scholar 

  33. Valentin J, van Zeeland J. Adaptive split-range control of a glasshouse heating system. Acta Hortic. 1980;106:109–16.

    Article  Google Scholar 

  34. Wang J, Zhou J, GuLi CRMPCR. Manage system for internet of things of greenhouse based on GWT. Inf Process Agric. 2018;5(2):269–78.

    Google Scholar 

  35. Wang YJ. Determination of all feasible robust PID controllers for open-loop unstable plus time delay processes with gain margin and phase margin specifications. ISA Trans. 2014;53(2):628–46.

    Article  PubMed  Google Scholar 

  36. Yang Y, Wang L. Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy Build. 2013;56:1–7.

    Article  CAS  Google Scholar 

  37. Zalzala AMS, Fleming PJ (1999) Genetic algorithms in engineering systems. IEE Control Ser UK.

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Correspondence to S. Revathi.

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This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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Revathi, S., Sivakumaran, N. & Radhakrishnan, T.K. Enhancement in Smart Operation of Greenhouse Environment Using Intelligent Biomimetic Control Framework. SN COMPUT. SCI. 5, 287 (2024). https://doi.org/10.1007/s42979-024-02611-z

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