TEMPERATURE CONTROLLER OPTIMIZATION BY COMPUTATIONAL INTELLIGENCE

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Žarko M. ĆOJBAŠIĆ Milan R. RISTANOVIĆ Stefan Ž. TEŠANOVIĆ Nemanja R. MARKOVIĆ

Abstract

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several metaheuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that metaheuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency.

Article Details

How to Cite
ĆOJBAŠIĆ, Žarko M. et al. TEMPERATURE CONTROLLER OPTIMIZATION BY COMPUTATIONAL INTELLIGENCE. Thermal Science, [S.l.], v. 20, p. S1541-S1552, feb. 2017. ISSN 2334-7163. Available at: <http://thermal-science.tech/journal/index.php/thsci/article/view/1670>. Date accessed: 17 oct. 2017. doi: https://doi.org/10.2298/TSCI16S5541Ć.
Section
Articles
Received 2017-02-07
Accepted 2017-02-07
Published 2017-02-07

References

[1] Shengwei, W., Intelligent Buildings and Building Automation, Spon Press, New York, USA, 2010
[2] Ma, Y., et al., Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Simulation and Experiments, IEEE Control Systems Magazine, 32 (2012), 1, pp. 44-64
[3] Hossein, M., et al., A Review of Intelligent Control Techniques in HVAC Systems, IEEE Energytech, Cleveland, O., USA, 2012, pp. 31-39
[4] Mirinejad, H., et al., Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems, Journal of Computer Science, 4 (2008), 9, pp. 777-783
[5] Astrom, K. J., Hagglund, T., PID Controllers: Theory, Design and Tuning, Instrument Society of America, Research Triangle Park, N. C., USA, 1995
[6] Huang, W, Lam, H. N., Using Genetic Algorithms to Optimize Controller Parameters for HVAC Systems, Energy and Buildings, 26 (1997), 3, pp. 277-282
[7] Alcala, R., et al., Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms, Applied Intelligence, 18 (2003), 2, pp. 155-177
[8] Alcala, R., et al., A Genetic Rule Weighting and Selection Process for Fuzzy Control of Heating, Ventilation and air Conditioning Systems, Engineering Applications of Artificial Intelligence, 18 (2005), 3, pp. 279-296
[9] Mossolly, M., et al., Optimal Control Strategy for a Multi-Zone Air Conditioning System Using a Genetic Algorithm, Energy, 34 (2009), 1, pp. 58-66
[10] Ursu, I., et al., Intelligent Control of HVAC Systems – Part I: Modeling and Synthesis, Incas Bulletin, 5 (2013), 1, pp. 103-118
[11] Parvaresh, A., et al., A New Mathematical Dynamic Model for HVAC System Components Based on Matlab/Simulink, International Journal of Innovative Technology and Exploring Engineering, 1 (2012), 2, pp. 1-6
[12] Alvsvag, O., HVAC-Systems: Modeling, Simulation and Control of HVAC-Systems, Master thesis, Norwegian University of Science and Technology of Trondheim, Norwege, 2011
[13] Debeljković, D., et al., Mathematical Model of Objects and Processes in Automatic Control Systems (in Serbian), Mechanical Engineering Faculty, University of Belgrade, Belgrade, Serbia, 2006
[14] Weitzmann, P., Modelling Building Integrated Heating and Cooling Systems, Ph. D. thesis, Technical University of Denmark, Lyng by Denmark, ISBN 87-7877-155-2, 2004
[15] Huang W. Z., et al., Dynamic Simulation of Energy Management Control Functions for HVAC Systems in Buildings, Energy Conversion & Management, 47 (2006), 7-8, pp. 926-943
[16] Tashtoush, B., et al., Dynamic Model of an HVAC System for Control Analysis, Energy, 30 (2005), 10, pp. 1729-1745
[17] Afram, A., Janabi-Sharifi, F., Review of Modeling Methods for HVAC Systems, Applied Thermal Engineering, 67 (2014), 1-2, pp. 507-519
[18] Mandić, P., et al., Modeling, Simulation and Control of Winter Regime of an Air Conditioning System in a Classroom, Proceedings, 44th International Congress on Heating, Refrigerating, and Air- Conditioning, SMEITS, Belgrade, 2014
[19] Edwards, C., Poslethwaite, I., Anti-Windup and Bumpless Transfer Schemas, Automatica, 34 (1998), 2, pp. 199-210
[20] Yu, B., van Paassen, A. H. C., Simulink and Bond Graph Modelling of an Air-Conditioned Room, Simulation Modelling Practice and Theory, 12 (2004), 1, pp. 61-76
[21] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Inc., Reston, Va., USA, 1989
[22] van Laarhoven, P. J., Aarts, E. H., Simulated Annealing: Theory and Applications, Springer, N. Y., USA, 1987
[23] García-Gonzalo, E., Fernandez-Martínez, J. L., A Brief Historical Review of Particle Swarm Optimization (PSO), Journal of Bioinformatics and Intelligent Control, 1 (2012), 1, pp. 3-16
[24] Dorigoa, M., Blum, C., Ant Colony Optimization Theory: A Survey, Theoretical Computer Science, 344 (2005), 2-3, pp. 243-278
[25] *** SRPS EN 15232:2014 – Energy Performance of Buildings – Impact of Building, Automation, Controls and Building Management, Institute for Standardization of Serbia, Belgrade
[26] Haq, A. U., Djurdjanović, D., Precedent-Free Fault Localization and Diagnosis for High Speed Train Drive Systems, Facta Universitatis – Series Mechanical Engineering, 13 (2015), 2, pp. 67-79

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