Microclimate control of a greenhouse by adaptive Generalized Linear Quadratic strategy

Mohamed Essahafi

Abstract


To highlight the conceptual aspects related to the implementation of techniques optimal control in the form state, we present in this paper, the identification and control of the temperature and humidity of the air inside a greenhouse. Using respectively an online identification based on the recursive least squares with forgotten Factor method and the multivariable adaptive linear quadratic Gaussian approach which the advanced technique (LQG) is presented.  The design of this controller parameters is based on state models identified directly from measured greenhouse data. hence the performances of the controller developed are illustrated by different tests and simulations on identified models of a greenhouse. Discussions on the results obtained are then processed in the paper to show the effectiveness of the controller in terms of stability and optimization of the cost of control.


Keywords


Adaptive control; linear quadratic Gaussian; recursive least squares; greenhouse; multivariable process; identification.

References


Revathi S, Radhakrishnan T K and Sivakumaran N, "Climate control in greenhouse using intelligent control algorithms," 2017 American Control Conference (ACC), Seattle, WA, 2017, pp. 887-892.

L. Meihui, D. Shangfeng, C. Lijun and H. Yaofeng, "Greenhouse multi-variables control by using feedback linearization decoupling method," 2017 Chinese Automation Congress (CAC), Jinan, China, 2017, pp. 604-608.

Q. Gao, X. Gao and X. Chen, "Analysis of current situation and existing problems in greenhouse environment control," Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, 2014, pp. 4516-4520.

E. J. van Henten and J. Bontsema, "Singular perturbation methods applied to a variational problem in greenhouse climate control," [1992] Proceedings of the 31st IEEE Conference on Decision and Control, Tucson, AZ, 1992, pp. 3068-3069 vol.4.

David Greenwood ; Michael J. Grimble “Multivariable LQG optimal control — Restricted structure control for benchmarking and tuning” European Control Conference (ECC), 2003 Print ISBN: 978-3-9524173-7-9 .

D. Lauri, J. V. Salcedo, S. Garcia-Nieto and M. Martinez, "Model predictive control relevant identification: multiple input multiple output against multiple input single output," in IET Control Theory & Applications, vol. 4, no. 9, pp. 1756-1766, September 2010.

X. Blasco, M. Martnez, J. M. Herrero, C. Ramos, J. Sanchis, "Model-based predictive control of greenhouse climate for reducing energy and water consumption", Computers and Electronics in Agriculture, vol. 55, Jan 2007.

Grimble M. J. 2000 “Restricted Structure LQG Optimal Control for Continuous Systems” IEE Proc. Control Theory Applications Vol 147 No2 (185-195) March.

Muhammad Ali Raza Anjum . “Adaptive System Identification using Markov Chain Monte Carlo.” TELKOMNIKA Indonesian Journal of Electrical Engineering Vol. 13, No. 1, January 2015, pp. 124

R. Garrido-Moctezuma, D. A. Suárez and R. Lozano, "Adaptive LQG control of positive real systems," 1997 European Control Conference (ECC), Brussels, 1997, pp. 144-149.

Grimble M. J. 2001 “Restricted structure controller performance assessment and benchmarkin”. American Control Conference 2718-2723.

Laroussi Oueslati « Commande multivariable d'une serre agricole par minimisation d'un critere quadratique » Phd thesis University of Toulon, 1990.

Mustapha Ait Lafkih, Zohra Zidane, Mohamed Ramzi “Design of Linear Quadratic Gaussian Controller for Sample Power System” International Journal of Emerging Technology and Advanced Engineering, Issue 3, March 2013.

M. Outanoute , A. Lachhab, “Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity Under Experimental Greenhouse”. International Journal of Electrical and Computer Engineering (IJECE) Vol. 6, No. 5, October 2016, pp

Tang Minan, Wang Xiaoming, Cao Jie , Li Ying “ Model Reference Adaptive Control Based on Lyapunov Stability Theory” TELKOMNIKA Indonesian Journal of Electrical Engineering Vol.12, No.3, March 2014, pp. 1812

Grimble M. J. and M. A. Johnson 1988 “Optimal Control and Stochastic Estimation” John Wiley and Sons Chichester UK Volume 1 and 2.

Zhiyong Zhang, Dongjian He1. “Adaptive Tracking Control Algorithm for Picking Wheel Robot” TELKOMNIKA, Vol.10, No.8, December 2012, pp. 2131~2138

M. Prandini and M. C. Campi, "A self-optimizing adaptive LQG control scheme for input-output systems," Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), Sydney, NSW, 2000, pp. 1110-1115 vol.2 doi: 10.1109/CDC.2000.912001

S.M.Veres, J.P.Norton, “Predictive self-tuning control by parameter bounding and worst-case design” Automatica Volume 29, Issue 4, July 1993, Pages 911-928.

H.T.Banks,ed., “Control and Estimation in Distributed Parameter Systems“(SIAM,Philadelphia, 1992).

M. Prandini and M. C. Campi, "A self-optimizing adaptive LQG control scheme for input-output systems," Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), Sydney, NSW, 2000, pp. 1110-1115 vol.2 doi: 10.1109/CDC.2000.912001




DOI: http://doi.org/10.11591/ijeecs.v11.i1.pp%25p
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