Optimal Intelligent Control for HVAC Systems
Abstract
In this paper a novel Optimal Fuzzy Proportional-Integral-Derivative Controller (OFPIDC) is designed for controlling the air supply pressure of Heating, Ventilation and Air-Conditioning (HVAC) system. The parameters of input membership functions, output polynomial functions of first-order Sugeno, and PID controller coefficients are optimized simultaneously by random inertia weight Particle Swarm Optimization (RNW-PSO). Simulation results show the superiority of the proposed controller than similar non-optimal fuzzy controllerReferences
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[2] Nguyen, H. T., Prasad, N. R., Walker, C. L., and Walker, E. A., A First Course in Fuzzy and Neural Control, USA: Chapman & Hall/ CRC, 2003.
[3] Zhi Qiao, W., Masaharu Mizumoto, Fuzzy sets and systems, 1996, vol. 78, pp. 23-35.
[4] Pedrycz, W., and de Oliveira, J. V., Optimization of fuzzy models, IEEE Trans. Syst., Man, Cyber., 1996, vol. 26, no.4, pp. 627 – 636.
[5] Hyun-Joon, C., Kwang-Bo, C., Bo-Hyeun, W., Fuzzy-PID hybrid control: automatic rule generation using genetic algorithm, Fuzzy sets and systems, 1997, vol. 92, no. 3, pp. 305-316.
[6] Alcala, R., Casillas, J., Cordon, O., Gonzalez, A., and Herrera, F., A genetic rule weighting and selection process for fuzzy control of heating, ventilation and air conditioning systems, Engineering application of Artificial Intelligence, 2005, vol. 28, pp. 279 – 296.
[7] Qiang, X., Wen-Jian, C., and Ming, H., A practical decentralized PID auto-tuning method for TITO systems under closed –loop control, International Journal of Innovative Computing, Information and Control, 2006, vol. 2, no. 2, pp. 305-322.
[8] Qing-Gao, W., Chang-Chieh, H., Yong, Z., and Qiang, B., Multivariable Controller Auto-Tuning with its Application in HVAC Systems, Proc. of the American Control Conf., California, 1999, vol. 6, pp. 4353 – 4357.
[9] Mudi, R.K., and Pal, N.R., A robust self-tuning scheme for PI and PD type fuzzy controllers, IEEE trans. on fuzzy sys., 1997, vol. 7, no. 1, pp. 2-16.
[10] Dirankov, D., Hellendorn, H., and Reintrank, M., An introduction to Fuzzy Control, New York: Spinger-Verlag, 1993.
[11] Jang, J., Sun, C., and Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, First Edition, Prentice Hall, 1997.
[12] Modares, H., Alfi, A., and Naghibi Sistani, M. B., Parameter Estimation of Bilinear Systems Based on an Adaptive Particle Swarm Optimization, Eng. Appl. Artifi. Intell., 2010, vol. 23, pp.1105-1111.
[13] Zhang, L., Yu, H., Hu, S., A New Approach to Improve Particle Swarm Optimization, Proc. of the international conf. on Genetic and evolutionary computation, 2003, 134-139.
[14] Shi, Y., Eberhart, R., A Modified Particle Swarm Optimizer, in Proc. of the IEEE Conf. On Evolutionary Computation, Singapore, 1998, pp. 69-73.
[15] Eberhart, R.C., and Shi, Y., Tracking and optimizing dynamic systems with particle swarms, in Proc. IEEE Congr. Evolutionary Computation , Seoul, Korea, 2001, pp. 94–97.
[16] Jian, W., and Wenjian, C., Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system, Syst., Man, Cybern., IEEE, 2000.
[17] Al-Fandi, M., Jaradat, M.A.K., and Sardahi, Y., Optimal PI-fuzzy logic controller of glucose concentration using genetic algoritm, International Journal of Knowledge-based and Intelligent Engineering Systems, 2011, vol. 15, pp. 99-117.
[18] A.K., Pal, and Mudi, R.K., Self-Tuning Fuzzy PI Controller and its Application to HVAC Systems, IJCC, 2008, vol. 6, no. 1, pp. 25-30.
Published
2012-10-01
How to Cite
KHOOBAN, Mohammad Hassan et al.
Optimal Intelligent Control for HVAC Systems.
Journal of Power Technologies, [S.l.], v. 92, n. 3, p. 192--200, oct. 2012.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/327>. Date accessed: 21 nov. 2024.
Issue
Section
Power Plant
Keywords
HVAC Systems; Sugeno-Type Fuzzy Inference; Fuzzy Proportional-Integral-Derivative Controller (OFPIDC); Random inertia weight Particle Swarm Optimization (RNW-PSO)
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