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
[1] Radakovic, Z. R., Milosevic, V. M., and Radakovic, S. B., Application of temperature fuzzy controller in an indirect resistance furnace, Applied Energy, 2002, vol. 73, pp. 167-182.
[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.
[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: 22 dec. 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)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).