Engineering Technologies and Systems - 04. Grudinin V.S., Khoroshavin V.S., Zotov A.V., Grudinin S.V. Adaptive Iterative Control of Temperature in Greenhouse

UDK 631.544.42:681.513.6

DOI: 10.15507/2658-4123.029.201903.383-395

Adaptive Iterative Control of Temperature in Greenhouse

Viktor S. Grudinin
Associate Professor of Chair of Electric Drive and Industrial Equipment Automation, Polytechnic Institute, Vyatka State University (36 Moskovskaya St., Kirov 610000, Russia) Ph.D. (Engineering), ResearcherID: G-5550-2018, ORCID: https://orcid.org/0000-0002-1615-6195, This email address is being protected from spambots. You need JavaScript enabled to view it.

Valeriy S. Khoroshavin
Professor of Chair of Electric Drive and Industrial Equipment Automation, Polytechnic Institute, Vyatka State University (36 Moskovskaya St., Kirov 610000, Russia), D.Sc. (Engineering), Professor, ResearcherID: G-5298-2018, ORCID: https://orcid.org/0000-0002-4355-3866, This email address is being protected from spambots. You need JavaScript enabled to view it.

Alexander V. Zotov
Associate Professor of Chair of Electric Drive and Industrial Equipment Automation, Polytechnic Institute, Vyatka State University (36 Moskovskaya St., Kirov 610000, Russia), Ph.D. (Engineering), ResearcherID: G-4912-2018, ORCID: https://orcid.org/0000-0002-9007-9861, This email address is being protected from spambots. You need JavaScript enabled to view it.

Sergey V. Grudinin
Postgraduate Student of Chair of Electric Drive and Industrial Equipment Automation, Polytechnic Institute, Vyatka State University (36 Moskovskaya St., Kirov 610000, Russia), ResearcherID: V-9221-2018, ORCID: https://orcid.org/0000-0003-1569-6808, This email address is being protected from spambots. You need JavaScript enabled to view it.

Introduction. Creation and development of efficient agricultural complexes providing high yields at minimal time, material, and energy costs is impossible without the use of automatic control systems (ACS), which allow for maintaining the microclimate of the greenhouse with high accuracy. Improvements of the microclimate by ASC are aimed at neutralizing the influence of parametric perturbations of processes inside and outside the greenhouse. Using the example of a temperature control channel in a greenhouse with a heating circuit based on hot piped water supply, an adaptive iterative (search) algorithm for adjusting the components of a proportional-integral-differential (PID) controller in the heating circuit is proposed to ensure the required quality of the control process.
Materials and Methods. The management is based on a parametrically uncertain model of temperature in the greenhouse, the structure of which, based on the principle of superposition, is transformed into a form with control and disturbances concentrated on an output coordinate. The use of an adaptive PID controller is based on the real-time analysis of a database containing trends of the controlled process. Using operators of Database Managment System or SQL language, queries evaluate regulation quality in accord with quality assessment. Proportional and differential components of the PID controller are adjusted so that the control system works on the verge of switching to auto-oscillation mode. The resulting static error is compensated by a change in the driving force.
Results. Simulation of the real structure of a single-circuit automatic control system with temperature in the greenhouse with built-in regulating, executive and measuring elements, with a delay of a coolant movement was carried out using the software MVTU (SimIn- Tech). The proposed adaptation algorithm, consisting of the additive adjustment of the PID controller coefficients and being conveniently implemented within the SCADA system, was shown to provide the minimum oscillatory temperature maintenance for arbitrary parametric perturbations in the presence of the delay.
Discussion and Conclusion. The proposed adaptation algorithm provides for compensation for model uncertainty and disturbances, while achieving the required accuracy of maintaining the temperature in the greenhouse. Results of the study will serve as the basics for development of multi-contour ACS microclimate greenhouses with the examination of the impact and compensation of parametric and structural uncertainty, inertia and nonlinearities of real elements. Our results may be used in many sectors of the national economy to study the general and applied problems of digital adaptive process control.

Keywords: microclimate, adaptive iterative algorithm, PID control law, simulation package MVTU

For citation: Grudinin V.S., Khoroshavin V.S., Zotov A.V., Grudinin S.V. Adaptive Iterative Control of Temperature in Greenhouse. Inzhenerernyye tekhnologii i sistemy = Engineering Technologies and Systems. 2019; 29(3):383-395. DOI: https://doi.org/10.15507/2658-4123.029.201903.383-395

Contribution of the authors: V. S. Grudinin ‒ formulation of the problem and choice of methods; V. S. Khoroshavin ‒ analysis of materials on the topic of the study; A. V. Zotov ‒ modeling the system; S. V. Grudinin ‒ programming and computer work.

All autors have read and approved the final manuscript.

Received 17.12.2018; revised 18.02.2019; published online 30.09.2019

REFERENCES

1. Grudinin V.S. Climate Control and Phytonomonitoring Tools. Agrarnaya nauka Yevro-Severo- Vostoka = Agrarian Science of Euro-North-East. 2007; (10):124-127. Available at: http://www.cnshb.ru/ jour/j_as.asp?id=96472 (accessed 22.01.2019). (In Russ.)

2. Gerasimov D.N., Lyzlova M.V. Adaptive Climate Control in Greenhouses. Izvestiya RAN. Teoriya i sistemy upravleniya = News of the Russian Academy of Sciences. Theory and Control Systems. 2014; (6):124-135. Available at: https://clck.ru/HHiDy (accessed 22.01.2019). (In Russ.)

3. Tokmakov N.M., Grudinin V.S. Mathematical Model for Microclimate's Control in Shed Greenhouses. Gavrish. 2008; 3:28-32. Available at: http://samodelkin.komi.ru/doc/6.pdf (accessed 22.01.2019). (In Russ.)

4. Von Zabeltitz C. Greenhouse Structures. In: Integrated Greenhouse Systems for Mild Climates. Springer, Berlin, Heidelberg; 2011. Рp. 59–135. (In Eng.) DOI: https://doi.org/10.1007/978-3-642-14582-7_5

5. Jones P., Jones J.W., Hwang Y. Simulation for Determining Greenhouse Temperature Set Points. Transactions of the ASAE. 1990; 33(5):1722-1728. Available at: http://scholar.google.ru/scholar?cluster= 7823325459120957027&hl=ru&as_sdt=0,5 (accessed 22.01.2019). (In Eng.)

6. Van Straten G., Van Henten E.J. Optimal Greenhouse Cultivation Control: Survey and Perspectives. IFAC Proceedings Volumes. 2010; 43(26):18-33. Available at: https://library.wur.nl/WebQuery/wurpubs/ fulltext/161860 (accessed 22.01.2019). (In Eng.)

7. Zeng S., Xu H.H.L., Li G. Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network. Sensors. 2012; (12):5328-5348. (In Eng.) DOI: https://doi.org/10.3390/s120505328

8. Seginer I., Boulard T., Bailey B.J. Neural Network Models of the Greenhouse Climate. Agricultural Engineering Research. 1994; 59:203-216. Available at: https://www.researchgate.net/ profile/T_Boulard/publication/222347971_Neural_Network_Models_of_the_Greenhouse_Climate/ links/5a0802894585157013a5e0ea/Neural-Network-Models-of-the-Greenhouse-Climate.pdf (accessed 22.01.2019). (In Eng.)

9. Kok R., Lacroix R., Clark G., Taillefer E. Imitation of a Procedural Greenhouse Model with an Artificial Neural Network. Canadian Agricultural Engineering. 1994; 36(2):117-126. Available at: https://scholar. google.ru/scholar?cluster=2098238280172549431&hl=ru&as_sdt=0,5 (accessed 22.01.2019). (In Eng.)

10. Grudinin V.S. Adaptive Computer Climate Control System. Agrarnaya nauka Yevro-Severo- Vostoka = Agrarian Science of Euro-North-East. 2007; (9):137-142. Available at: http://www.cnshb.ru/ jour/j_as.asp?id=92776 (accessed 22.01.2019). (In Russ.)

11. Ferreira P.M., Ruano A.E. Discrete Model Based Greenhouse Environmental Control Using the Branch & Bound Algorithm. The International Federation of Automatic Control. 2008. р. 2937-2943. Available at: http://folk.ntnu.no/skoge/prost/proceedings/ifac2008/data/papers/3461.pdf (accessed 22.01.2019). (In Eng.)

12. Fitz-Rodriguez E., Kubota C., Giacomelli G.A., et al. Dynamic Modeling and Simulation of Greenhouse Environments under Several Scenarios: a Web-Based Application. Computers and Electronics in Agriculture. 2010; (70):105-116. Available at: http://irrecenvhort.ifas.ufl.edu/Teaching%20publications/ Publications/COMPAG-2009-Greenhouse%20simulation.pdf (accessed 22.01.2019). (In Eng.)

13. Berenguel M., Yebra L.J., Rodriguez F. Adaptive Control Strategies for Greenhouse Temperature Control. En viado a ECC. 2003. р. 2747-2752. (In Eng.) DOI: https://doi.org/10.23919/ECC.2003.7086457

14. Meerov M.V. Adaptive Compensating Controllers with Smiths Predictor. Avtomatika i telemekhanika = Automation and Remote Control. 2000; (10):125-135. Available at: http://www.mathnet.ru/php/archive. phtml?wshow=paper&jrnid=at&paperid=15286&option_lang=rus (accessed 22.01.2019). (In Russ.)

15. Denisenko V.V. PID Regulators: Principles of Construction and Modification. Sovremennye tekhnologii avtomatizatsii = Modern Automation Technology. 2007; (1):78-88. Available at: http:// www.studmed.ru/denisenko-vv-pid-regulyatory-principy-postroeniya-i-modifikacii_dc7431cf063.html (accessed 22.01.2019). (In Russ.)

16. Åström K.J., Hägglund T. Advanced PID Control. ISA Publication. 2006. Available at: http:// intranet.ceautomatica.es/sites/default/files/upload/13/files/AdvancesInPIDControl_KJA.pdf (accessed 22.01.2019). (In Eng.)

17. Karpenko A.V., Petrova I.Yu. Indoor Climate Control Models. Fundamental Research. 2016; (7):224-229. Available at: http://fundamental-research.ru/ru/article/view?id=40488 (accessed 22.01.2019). (In Russ.)

18. Gudkova N.V. Application of the Principles of Adaptive Modeling to Control Problems of Dynamic Objects of the “Black Box” Type. Sovremennaya elektronika = Modern Electronics. 2012; (8):68-70. Available at: http://www.radiofiles.ru/news/sovremennaja_ehlektronika_8_2012/2012-10-09-2397 (accessed 22.01.2019). (In Russ.)

19. Mokrushin S.A., Khoroshavin V.S., Ohapkin S.I., et al. The Analysis of Controllability and Stability of an Approximate Model of Heat Transfer in an Autoclave. Vestnik Mordovskogo universiteta = Mordovia University Bulletin. 2018; 28(3):416-428. (In Russ.) DOI: https://doi.org/10.15507/0236-2910.028.201803.416-428

20. Alonso A.A., Banga J.R., Perez-Martin R. Modeling and Adaptive Control for Batch Sterilization. Computers & Chemical Engineering. 1998; 22(3):445-458. Available at: https://www.sciencedirect.com/ science/article/pii/S0098135497002500 (accessed 22.01.2019). (In Eng.)

21. Kirgin D.S. Algorithms for the Control of the Technological Process of Vulcanization of an Autoclave Installation. Vestnik IrGTU = Proceedings of Irkutsk State Technical University. 2011; 55(8):195-199. Available at: http://cyberleninka.ru/article/n/algoritmy-upravleniya-tehnologicheskim- protsessom-vulkanizatsii-ustanovki-avtoklav (accessed 22.01.2019). (In Russ.)

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