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PYTHAGORAS : Jurnal Matematika dan Pendidikan Matematika

Kendali Model Prediktif Kokoh pada Model Suhu Rumah Kaca

Keywords

Greenhouse, MPC, RMPC, PSO Algorithm, Genetic Algorithm

Document Type

Article

Abstract

Dalam paper ini dibahas mengenai masalah sistem kendali suhu rumah kaca dengan mempertimbangkan variabel gangguan. Masalah kendali suhu rumah kaca ini dimodelkan dengan RMPC (Robust Model Predictive Control). Algoritma Particle Swarm Optimization (PSO) dan Genetic Algortihm (GA) digunakan untuk mencari penyelesaian masalah RMPC pada sistem suhu rumah kaca yang berupa masalah optimisasi berkendala. Berdasarkan hasil simulasi, teknik kendali RMPC mampu mengatur suhu rumah kaca sesuai suhu yang diinginkan dengan gangguan relatif kecil yaitu sebesar 0.09oC. Selain itu, waktu iterasi Algoritma PSO lebih cepat dalam menyelesaikan masalah RMPC pada sistem suhu rumah kaca dibandingkan dengan Algoritma Genetika.

Page Range

425-440

Issue

2

Volume

17

Digital Object Identifier (DOI)

10.21831/pythagoras.v17i2.51414

Source

https://journal.uny.ac.id/index.php/pythagoras/article/view/51414

References

Abbasi, S., Rahmani, A. M., Balador, A., & Sahafi, A. (2023). A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles. Applied Soft Computing, 143, 110413. https://doi.org/10.1016/j.asoc.2023.110413

Bajpai, P., & Kumar, M. (2010). Genetic algorithm-an approach to solve global optimization problems. Indian Journal of Computer Science and Engineering, 1(3), 199-206.

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(3), 329-338. https://doi.org/10.1016/j.inpa.2018.04.003

Chen, Q., & Hu, X. (2022). Design of intelligent control system for agricultural greenhouses based on adaptive improved genetic algorithm for multi-energy supply system. Energy Reports, 8, 12126-12138. https://doi.org/10.1016/j.egyr.2022.09.018

Chen, W. H., & You, F. (2021). Smart greenhouse control under harsh climate conditions based on data-driven robust model predictive control with principal component analysis and kernel density estimation. Journal of Process Control, 107, 103-113. https://doi.org/10.1016/j.jprocont.2021.10.004

Choab, N., Allouhi, A., El Maakoul, A., Kousksou, T., Saadeddine, S., & Jamil, A. (2019). Review on greenhouse microclimate and application: Design parameters, thermal modeling and simulation, climate controlling technologies. Solar Energy, 191(August), 109-137. https://doi.org/10.1016/j.solener.2019.08.042

Diveev, A. I., & Bobr, O. V. (2017). Variational Genetic Algorithm for NP-hard Scheduling Problem Solution. Procedia Computer Science, 103(October 2016), 52-58. https://doi.org/10.1016/j.procs.2017.01.010

Gao, Y., Gray, A., Tseng, H. E., & Borrelli, F. (2014). A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles. Vehicle System Dynamics, 52(6), 802-823. https://doi.org/10.1080/00423114.2014.902537

González, R., Rodríguez, F., Guzmán, J. L., & Berenguel, M. (2014). Robust constrained economic receding horizon control applied to the two time-scale dynamics problem of a greenhouse. Optimal Control Applications and Methods, 35(4), 435-453. https://doi.org/10.1002/oca.2080

Hasni, A., Taibi, R., Draoui, B., & Boulard, T. (2011). Optimization of greenhouse climate model parameters using particle swarm optimization and genetic algorithms. Energy Procedia, 6, 371-380. https://doi.org/10.1016/j.egypro.2011.05.043

Janatian, N., & Sharma, R. (2023). A robust model predictive control with constraint modification for gas lift allocation optimization. Journal of Process Control, 128, 102996. https://doi.org/10.1016/j.jprocont.2023.102996

Ma, T., Liu, S., & Xiao, H. (2020). Location of natural gas leakage sources on offshore platform by a multi-robot system using particle swarm optimization algorithm. Journal of Natural Gas Science and Engineering, 84(September). https://doi.org/10.1016/j.jngse.2020.103636

Maciejowski, J. M., Goulart, P. J., & Kerrigan, E. C. (2007). Constrained control using model predictive control. In Lecture Notes in Control and Information Sciences (Vol. 346, pp. 273-291). https://doi.org/10.1007/978-3-540-37010-9_9

Mahmood, F., Govindan, R., Bermak, A., Yang, D., & Al-Ansari, T. (2023). Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment. Applied Energy, 343(May), 121190. https://doi.org/10.1016/j.apenergy.2023.121190

Mahmood, F., Govindan, R., Yang, D., Bermak, A., & Al-Ansari, T. (2022). Forecasting cooling load and water demand of a semi-closed greenhouse using a hybrid modelling approach. International Journal of Ambient Energy, 43(1), 8046-8066. https://doi.org/10.1080/01430750.2022.2088617

Mirzaei, M., Poulsen, N. K., & Niemann, H. H. (2012). Robust model predictive control of a wind turbine. Proceedings of the American Control Conference, 4393-4398. https://doi.org/10.1109/acc.2012.6314887

Morato, M. M. (2023). A robust model predictive control algorithm for input-output LPV systems using parameter extrapolation. Journal of Process Control, 128, 103021. https://doi.org/10.1016/j.jprocont.2023.103021

Nikolaou, G., Neocleous, D., Christou, A., Kitta, E., & Katsoulas, N. (2020). Implementing sustainable irrigation in water-scarce regions under the impact of climate change. Agronomy, 10(8), 1-33. https://doi.org/10.3390/agronomy10081120

Oh, T. H., Kim, J. W., Son, S. H., Jeong, D. H., & Lee, J. M. (2022). Multi-strategy control to extend the feasibility region for robust model predictive control. Journal of Process Control, 116, 25-33. https://doi.org/10.1016/j.jprocont.2022.05.011

Racovic, S. V., W. S. L. (2007). Handbook of Model Predictive Control. In IEEE Control Systems (Vol. 40, Issue 5). https://doi.org/10.1109/mcs.2020.3005257

Rhouma, A., & Bouani, F. (2015). Robust predictive controller based on an uncertain fractional order model. 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015, 1, 1-5. https://doi.org/10.1109/SSD.2015.7348175

Saltık, M. B., Özkan, L., Ludlage, J. H. A., Weiland, S., & Van den Hof, P. M. J. (2018). An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects. Journal of Process Control, 61, 77-102. https://doi.org/10.1016/j.jprocont.2017.10.006

Sánchez-Amores, A., Chanfreut, P., Maestre, J. M., & Camacho, E. F. (2023). Robust coalitional model predictive control with negotiation of mutual interactions. Journal of Process Control, 123, 64-75. https://doi.org/10.1016/j.jprocont.2023.01.017

Su, B., Lin, Y., Wang, J., Quan, X., Chang, Z., & Rui, C. (2022). Sewage treatment system for improving energy efficiency based on particle swarm optimization algorithm. Energy Reports, 8, 8701-8708. https://doi.org/10.1016/j.egyr.2022.06.053

Thangaraj, R., Pant, M., Abraham, A., & Snasel, V. (2012). Modified Particle Swarm Optimization with time varying velocity vector. International Journal of Innovative Computing, Information and Control, 8(1 A), 201-218.

Vlašić, I., Ðurasević, M., & Jakobović, D. (2019). Improving genetic algorithm performance by population initialisation with dispatching rules. Computers and Industrial Engineering, 137(March), 106030. https://doi.org/10.1016/j.cie.2019.106030

Went, F. W. (1953). The Effect of Temperature on Plant Growth. Annual Review of Plant Physiology, 4(1), 347-362. https://doi.org/10.1146/annurev.pp.04.060153.002023

Xu, L. (2021). Research on computer interactive optimization design of power system based on genetic algorithm. Energy Reports, 7, 1-13. https://doi.org/10.1016/j.egyr.2021.10.085

Yan, Z., & Wang, J. (2014). Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks. IEEE Transactions on Neural Networks and Learning Systems, 25(3), 457-469. https://doi.org/10.1109/TNNLS.2013.2275948

Ye, C., Chen, L., Ni, S., & Zhou, J. (2021). Evaluation model of forest eco economic benefits based on discrete particle swarm optimization. Environmental Technology and Innovation, 22, 101426. https://doi.org/10.1016/j.eti.2021.101426

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