Fuzzy Control of the Air Conditioning System at Different Operating Pressures
The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.
[1] Mirinejad, H., S.H. Sadati, S. Hasanzadeh, A.M. Shahri and M. Ghasemian, 2008. Design and simulation of an automated system for
greenhouse using LabVIEW. AEJAES, 3: 279-284.
[2] Guo, P.Y., Z.H. Guang and Z. Bien, 1998. A simple fuzzy adaptive control method and application in HVAC. Proceeding of the IEEE
International Conference on Fuzzy System, IEEE World Congress on Computational Intelligence, May 4-9, Anchorage, Alaska, USA., pp:
528-532.
[3] Huang, S. and R.M. Nelson, 1991. A PID-law combining fuzzy
controller for HVAC application. ASHRAE Trans., 97: 768-774.
[4] Geng, G. and G.M. Geary, 1993. On performance and tuning of PID
controllers in HVAC systems. Proceeding of the 2nd IEEE Conference on Control Applications, Sep. 13-16, Vancouver, B.C., Canada, pp: 819-824.
[5] Pinnella, M.J., E. Wechselberger, D.C. Hittle and C.O. Pederson, 1986.
Self-tuning digital integral control. ASHRAE Trans., 2: 202-210.
[6] Krakow, K.I. and S. Lin, 1995. PI control of fan speed to maintain
constant discharge pressure. ASHRAE Trans. Res., 101: 398-407.
[7] Wang, Q.G., C.C. Hang, Y. Zhang and Q. Bi, 1999. Multivariable controller auto-tuning with its application in HVAC systems.
Proceedings of the Conference on American Control, June 02-04, San
Diego, CA, USA. pp: 4353-4357.
[8] Dexter, L., G. Geng and P. Haves, 1990. The application of self-tuning
PID control to HVAC systems. Proceeding of the Colloquium on Control in Building Energy Management Systems, May 30, London, UK., pp: 4/1-4/3
[9] Arguello-Serrano B. and M. Velez-Reyes. 1999. Nonlinear control of a
heating, ventilating and air conditioning system with thermal load
estimation. IEEE Trans. Control Syst. Technol., 7: 56-63.
[10] Semsar, E., M.J. Yazdanpanah and C. Lucas, 2003. Nonlinear control
and disturbance decoupling of an HVAC system via feedback
linearization and backstepping. Proceedings of the IEEE Conference on
Control Applications, June 23-25, Istanbul, Turkey, pp: 646-650.
[11] House, J.M. and T.F. Smith, 1995. Optimal control of building and
HVAC systems Proceedings of the Conference on American Control,
June 21-23, Seattle, WA., USA., pp: 4326-4330.
[12] Nizet, J.L., J. Lecomte and F.X. Litt, 1984. Optimal control applied to
air conditioning in buildings, ASHRAE Trans., 90: 587-600.
[13] Hunt K.J., D. Sbarbaro, R. Zbikowski and P.J. Gawthrop, 1992. Neural
networks for control systems-a survey. Automatica (J. IFAC)., 28: 1083-1112.
[14] Teeter, J. and M. Chow, 1998. Application of functional link neural
network to HVAC thermal dynamic system identification. Trans. Fuzzy
Syst., 45: 170-176.
[15] Shah, R., A. Alleyne, and C. Bullard, “Dynamic Modeling and Control
of Multi-Evaporator Air Conditioning Systems,” ASHRAE
Transactions, 110:1, 109-119, April 2004.
[16] Ploplys, N., P. Kawka, and A. Alleyne, “Closed Loop Control Over
Wireless Networks,” IEEE Control Systems, 24:3, 58-71, June 2004.
[17] Rasmussen, B., A. Musser, and A. Alleyne, “Model-Driven System
Identification of Transcritical Vapor Compression Systems,” IEEE
Transactions on Control Systems Technology, 13:3, 444-451, May 2005.
[18] Eldredge, B., B. Rasmussen, and A. Alleyne, “Automotive Vapor
Compression Cycles: Validation of Control-Oriented Models,” SAE
2006 Transactions, Journal of Engines, 2006-01-1452, 2007.
[19] McKinley, T. and A. Alleyne, “An Advanced Nonlinear Switched Heat
Exchanged Model for Vapor Compression Cycles using the Moving
Boundary Method,” International Journal of Refrigeration, Vol. 31, No.
7, pp. 1253-1264, Nov. 2008.
[20] Eldredge, B., B. Rasmussen and A. Alleyne, “Moving-boundary Heat
Exchanger Models with Variable Outlet Phase,” ASME Journal of
Dynamic Systems Measurement and Control, Vol. 130, No. 6 Article
Number: 061003, Nov. 2008.
[21] Jain N., B. Li, M. Keir., B. Hencey, and A. Alleyne, “Decentralized
Feedback Structures of a Vapor Compression Cycle System,” IEEE
Transactions on Control Systems Technology, Vol. 18, No. 1, pp: 185-
193, Jan 2010.
[22] Rasmussen, B. P. and A. Alleyne, “Gain Scheduled Control of an Air
Conditioning System using the Youla Parameterization,” IEEE
Transactions on Control Systems Technology, DOI
10.1109/TCST.2009.2035104.
[23] Li, Bin and A. Alleyne, “A Dynamic Model of a Vapor Compression
Cycle with Shut-down and Start-up Operations,” International Journal of
Refrigeration, Vol 33, No 3, pp 538-552, May 2010.
[24] Li, B., V. Chandan, B. Mohs, and A.G. Alleyne, “Optimal On-Off
control of an Air Conditioning and Refrigeration Systems,” IFAC
Control Engineering Practice, 2010.
[25] Shah, R., B. Rasmussen, and A. Alleyne, “Application of Multivariable
Adaptive Control to Automotive Air Conditioning Systems,”
International Journal of Adaptive Control and Signal Processing, 18:2,
199-221, March 2004.
[26] Al-Nimr, M.A., Daqqaq, M. F. and Hader, M. A.: Effect of working
fluids on the performance of a novel summer air conditioning system,
Int. Communications in Heat and Mass Transfer, Vol. 28(4), pp. 565-
573 (2001).
[27] Hader, M., Al-Nimr, M.A., and Daqqaq, M. F.: Effect of operating
pressure on the performance of a novel summer air conditioning system,
IASTED International Conference on Power and Energy Systems
(EuroPES2001), Rhodes-Greece (2001).
[28] Al-Nimr, M.A., Abu Nabah, B. A. and Naji, M.: A novel summer air
conditioning system, Energy Conversion and Management, Vol. 43(14),
pp. 1911-1921 (2002).
[29] Saleh, A. and Al-Nimr, M. A., A modified air jet refrigeration system,
Int. J. Heat & Technology, Vol. 24(2), pp. 23-28 (2006).
[30] Saleh, A. and Al-Nimr, M. A.: Evaporative system for water and
beverage refrigeration in hot countries, Journal of Power and Energy,
Vol. 221(8), pp. 1099-1105 (2007).
[31] Saleh, A. and Al-Nimr, M. A.: The Effectiveness of Multi Stage
Dehumidification-humidification for Improving Cooling Ability of
Evaporative Air Conditioning, Accepted for publication in J. Power and
Energy, Part A, (2008).
[32] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis
with a fuzzy logic controller, International Journal of Man-Machine
Studies, vol. 7, pp. 1–13 (1975).
[33] M. Sugeno and G.T. Kang, Structure identification of fuzzy model,
Fuzzy Sets and Systems, vol. 28, pp. 15–33 (1988).
[34] T. Takagi and M. Sugeno, Fuzzy identification of systems and its
application to modeling and control, IEEE Transactions on Systems,
Man, and Cybernetics, vol. 15, no. 1, pp. 116–132 (1985).
[35] Hao Ying, Theory and application of a novel fuzzy PID controller using
a simplified Takagi-Sugeno rule scheme, Information Sciences, vol. 123,
281-293 (2000).
[1] Mirinejad, H., S.H. Sadati, S. Hasanzadeh, A.M. Shahri and M. Ghasemian, 2008. Design and simulation of an automated system for
greenhouse using LabVIEW. AEJAES, 3: 279-284.
[2] Guo, P.Y., Z.H. Guang and Z. Bien, 1998. A simple fuzzy adaptive control method and application in HVAC. Proceeding of the IEEE
International Conference on Fuzzy System, IEEE World Congress on Computational Intelligence, May 4-9, Anchorage, Alaska, USA., pp:
528-532.
[3] Huang, S. and R.M. Nelson, 1991. A PID-law combining fuzzy
controller for HVAC application. ASHRAE Trans., 97: 768-774.
[4] Geng, G. and G.M. Geary, 1993. On performance and tuning of PID
controllers in HVAC systems. Proceeding of the 2nd IEEE Conference on Control Applications, Sep. 13-16, Vancouver, B.C., Canada, pp: 819-824.
[5] Pinnella, M.J., E. Wechselberger, D.C. Hittle and C.O. Pederson, 1986.
Self-tuning digital integral control. ASHRAE Trans., 2: 202-210.
[6] Krakow, K.I. and S. Lin, 1995. PI control of fan speed to maintain
constant discharge pressure. ASHRAE Trans. Res., 101: 398-407.
[7] Wang, Q.G., C.C. Hang, Y. Zhang and Q. Bi, 1999. Multivariable controller auto-tuning with its application in HVAC systems.
Proceedings of the Conference on American Control, June 02-04, San
Diego, CA, USA. pp: 4353-4357.
[8] Dexter, L., G. Geng and P. Haves, 1990. The application of self-tuning
PID control to HVAC systems. Proceeding of the Colloquium on Control in Building Energy Management Systems, May 30, London, UK., pp: 4/1-4/3
[9] Arguello-Serrano B. and M. Velez-Reyes. 1999. Nonlinear control of a
heating, ventilating and air conditioning system with thermal load
estimation. IEEE Trans. Control Syst. Technol., 7: 56-63.
[10] Semsar, E., M.J. Yazdanpanah and C. Lucas, 2003. Nonlinear control
and disturbance decoupling of an HVAC system via feedback
linearization and backstepping. Proceedings of the IEEE Conference on
Control Applications, June 23-25, Istanbul, Turkey, pp: 646-650.
[11] House, J.M. and T.F. Smith, 1995. Optimal control of building and
HVAC systems Proceedings of the Conference on American Control,
June 21-23, Seattle, WA., USA., pp: 4326-4330.
[12] Nizet, J.L., J. Lecomte and F.X. Litt, 1984. Optimal control applied to
air conditioning in buildings, ASHRAE Trans., 90: 587-600.
[13] Hunt K.J., D. Sbarbaro, R. Zbikowski and P.J. Gawthrop, 1992. Neural
networks for control systems-a survey. Automatica (J. IFAC)., 28: 1083-1112.
[14] Teeter, J. and M. Chow, 1998. Application of functional link neural
network to HVAC thermal dynamic system identification. Trans. Fuzzy
Syst., 45: 170-176.
[15] Shah, R., A. Alleyne, and C. Bullard, “Dynamic Modeling and Control
of Multi-Evaporator Air Conditioning Systems,” ASHRAE
Transactions, 110:1, 109-119, April 2004.
[16] Ploplys, N., P. Kawka, and A. Alleyne, “Closed Loop Control Over
Wireless Networks,” IEEE Control Systems, 24:3, 58-71, June 2004.
[17] Rasmussen, B., A. Musser, and A. Alleyne, “Model-Driven System
Identification of Transcritical Vapor Compression Systems,” IEEE
Transactions on Control Systems Technology, 13:3, 444-451, May 2005.
[18] Eldredge, B., B. Rasmussen, and A. Alleyne, “Automotive Vapor
Compression Cycles: Validation of Control-Oriented Models,” SAE
2006 Transactions, Journal of Engines, 2006-01-1452, 2007.
[19] McKinley, T. and A. Alleyne, “An Advanced Nonlinear Switched Heat
Exchanged Model for Vapor Compression Cycles using the Moving
Boundary Method,” International Journal of Refrigeration, Vol. 31, No.
7, pp. 1253-1264, Nov. 2008.
[20] Eldredge, B., B. Rasmussen and A. Alleyne, “Moving-boundary Heat
Exchanger Models with Variable Outlet Phase,” ASME Journal of
Dynamic Systems Measurement and Control, Vol. 130, No. 6 Article
Number: 061003, Nov. 2008.
[21] Jain N., B. Li, M. Keir., B. Hencey, and A. Alleyne, “Decentralized
Feedback Structures of a Vapor Compression Cycle System,” IEEE
Transactions on Control Systems Technology, Vol. 18, No. 1, pp: 185-
193, Jan 2010.
[22] Rasmussen, B. P. and A. Alleyne, “Gain Scheduled Control of an Air
Conditioning System using the Youla Parameterization,” IEEE
Transactions on Control Systems Technology, DOI
10.1109/TCST.2009.2035104.
[23] Li, Bin and A. Alleyne, “A Dynamic Model of a Vapor Compression
Cycle with Shut-down and Start-up Operations,” International Journal of
Refrigeration, Vol 33, No 3, pp 538-552, May 2010.
[24] Li, B., V. Chandan, B. Mohs, and A.G. Alleyne, “Optimal On-Off
control of an Air Conditioning and Refrigeration Systems,” IFAC
Control Engineering Practice, 2010.
[25] Shah, R., B. Rasmussen, and A. Alleyne, “Application of Multivariable
Adaptive Control to Automotive Air Conditioning Systems,”
International Journal of Adaptive Control and Signal Processing, 18:2,
199-221, March 2004.
[26] Al-Nimr, M.A., Daqqaq, M. F. and Hader, M. A.: Effect of working
fluids on the performance of a novel summer air conditioning system,
Int. Communications in Heat and Mass Transfer, Vol. 28(4), pp. 565-
573 (2001).
[27] Hader, M., Al-Nimr, M.A., and Daqqaq, M. F.: Effect of operating
pressure on the performance of a novel summer air conditioning system,
IASTED International Conference on Power and Energy Systems
(EuroPES2001), Rhodes-Greece (2001).
[28] Al-Nimr, M.A., Abu Nabah, B. A. and Naji, M.: A novel summer air
conditioning system, Energy Conversion and Management, Vol. 43(14),
pp. 1911-1921 (2002).
[29] Saleh, A. and Al-Nimr, M. A., A modified air jet refrigeration system,
Int. J. Heat & Technology, Vol. 24(2), pp. 23-28 (2006).
[30] Saleh, A. and Al-Nimr, M. A.: Evaporative system for water and
beverage refrigeration in hot countries, Journal of Power and Energy,
Vol. 221(8), pp. 1099-1105 (2007).
[31] Saleh, A. and Al-Nimr, M. A.: The Effectiveness of Multi Stage
Dehumidification-humidification for Improving Cooling Ability of
Evaporative Air Conditioning, Accepted for publication in J. Power and
Energy, Part A, (2008).
[32] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis
with a fuzzy logic controller, International Journal of Man-Machine
Studies, vol. 7, pp. 1–13 (1975).
[33] M. Sugeno and G.T. Kang, Structure identification of fuzzy model,
Fuzzy Sets and Systems, vol. 28, pp. 15–33 (1988).
[34] T. Takagi and M. Sugeno, Fuzzy identification of systems and its
application to modeling and control, IEEE Transactions on Systems,
Man, and Cybernetics, vol. 15, no. 1, pp. 116–132 (1985).
[35] Hao Ying, Theory and application of a novel fuzzy PID controller using
a simplified Takagi-Sugeno rule scheme, Information Sciences, vol. 123,
281-293 (2000).
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:53762", author = "Mohanad Alata and Moh'd Al-Nimr and Rami Al-Jarrah", title = "Fuzzy Control of the Air Conditioning System at Different Operating Pressures", abstract = "The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.
", keywords = "Air Conditioning, ANFIS, Fuzzy Control, Sugeno System.", volume = "6", number = "11", pages = "2383-7", }
