Development an Adaptive Incremental Fuzzy PI Controller for a HVAC System

  • Jianbo Bai College of Mechanical and Electrical Engineering

Abstract

This paper presents an adaptive incremental fuzzy PI controller (AIFPI) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. The HVAC system has two subsystems and is used to control indoor temperature and humidity in a thermal zone. As the system has strong-coupling and non-linear characteristics, fixed PI controllers have poor control performance and more energy consumption. Aiming to solve the problem, fuzzy control and PI control are combined together organically. In the proposed control scheme, the error of the system output and its derivative are taken as two parameters necessary to adapt the proportional (P) and integral (I) gains of the PI controller based on fuzzy reasoning according to practical control experiences. To evaluate the effectiveness of the proposed control methods in the HVAC system, it is compared with a fixed well-tuned PI controller. The results demonstrate that the AIFPI controller has more superior performance than the latter.

Author Biography

Jianbo Bai, College of Mechanical and Electrical Engineering
Department of Mathematics and Computer Science

References

[1] Underwood, C.P., HVAC control systems: modeling, analysis and design, London and New York, E & FN Spon, 1999.
http://dx.doi.org/10.4324/9780203237168

[2] Salsbury, T., A survey of control technologies in the building automation industry, Proc. of the 16th IFAC World Congress, 331–341, 2005.

[3] Levine, W.S., Control System Applications, Boca Raton, CRC Press, 121-123, 2000.

[4] Astrom, K.J., Wittenmark, B., Adaptive control, Reading, Addison-Wesley, 1995.

[5] Bai, J., Zhang. X., A new adaptive PI controller and its application in HVAC systems, Energy Conversion and Management, 48(2): 1043-1054, 2007.
http://dx.doi.org/10.1016/j.enconman.2006.10.023

[6] Nesler, C.G., Automated Controller Tuning for HVAC Applications, ASHRAE Trans., 92(2B): 189-201, 1986.

[7] White, D.A., Sofge, D.A., Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, Van Nostrand, Reinhold Comp., 1992.

[8] Ferreira, P.M., Ruano, A.E., Silva S., Neural networks based predictive control for thermal comfort and energy savings in public buildings, Energy and Buildings, 55(0): 238-251, 2012.
http://dx.doi.org/10.1016/j.enbuild.2012.08.002

[9] Zaheer-uddin, M., Tudoroiu, N., Neuro-PID tracking control of a discharge airtemperature system, Energy Conversion and Management, 45(15-16): 2405-2415, 2004.
http://dx.doi.org/10.1016/j.enconman.2003.11.016

[10] Seem, J.E., Haugstad, H.J., Field and Laboratory Results for a New Pattern Recognition Adaptive Controller, Proc. of Clima 2000 Conference, 96-100, 2000.

[11] Chang, S.S.L., Zadeh, L.A., On fuzzy mapping and control. Systems, Man and Cybernetics, IEEE Trans. on, (1): 30-34, 1972.

[12] Arguello-Serrano, B., Velez-Reyes, M., Nonlinear control of a heating, ventilating, and air conditioning system with thermal load estimation, IEEE Transactions on Control Systems Technology, 7(1): 56-63, 1999.
http://dx.doi.org/10.1109/87.736752

[13] Wang, Q.G., Lee T.H., Fung, H.W. et al., PID tuning for improved performance, IEEE Trans. on Control Syst. Tech., 7(2): 457-465, 1999.

[14] Bi, Q., Cai W.J., Wang, Q.G. et al., Advanced controller auto-tuning and its application in HVAC systems, Control Eng. Practice, 8(6): 633-644, 2000.
http://dx.doi.org/10.1016/S0967-0661(99)00198-7
Published
2013-09-17
How to Cite
BAI, Jianbo. Development an Adaptive Incremental Fuzzy PI Controller for a HVAC System. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 8, n. 5, p. 654-661, sep. 2013. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/641>. Date accessed: 06 aug. 2020. doi: https://doi.org/10.15837/ijccc.2013.5.641.

Keywords

HVAC system, adaptive control, fuzzy logic control, PI control