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MIMO fuzzy logic controller design for temperature regulation of HVAC systems in cooling mode subjected to time-varying natural ventilation loads

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Abstract

A fuzzy logic control strategy for temperature and relative humidity control of air-handling unit in heating, ventilating, and air-conditioning systems is proposed. Those systems operating in the cooling process are multi-variable and nonlinear systems and their control is very difficult. The dynamics of all elements of the system are presented and the case of an unknown external disturbance is also considered. The zone temperature is regulated by controlling the flow rate of cold water passing through the cooling coil. The numerical results for the uncontrolled, On–Off controlled, and Proportional-Integral-Derivative (PID) controlled systems are compared with the proposed fuzzy logic controlled system under the weather conditions of Istanbul, Turkey in summer season. From the numerical results, it was observed that for the zone temperature the 78 min settling time of the uncontrolled case was reduced to the 26 min by the proposed fuzzy logic controller, which yielded the best result among the other controllers. The PID and fuzzy logic controllers provided smoother control signals when compared to the fluctuating control signal of the On–Off controller for the flow rate of cold water. The energy consumptions of the controllers, normalized with respect to the uncontrolled case, were also compared to show the expense of achieving fast time response. The increase is only between 2.5–7.4% and the proposed fuzzy logic controller used less energy than the PID controller.

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Abbreviations

AHU:

Air-handling unit

FLC:

Fuzzy logic control

HVAC:

Heating, ventilating, and air-conditioning

MIMO:

Multi-input multi-output

PID:

Proportional integral derivative

A:

Area (m2)

c:

Constants for cooling coil (c1 to c5)

C:

Constants for psychometric relations (C8 to C13)

C:

Overall thermal capacitance (J/°C)

c:

Specific heat (J/kg °C)

cp :

Specific heat of air (J/kg °C)

CR :

Overall thermal capacitance of roof (J/°C)

d:

Humidity disturbance (m3/s)

f:

Frequency (Hz)

h:

Heat transfer coefficient (W/m2 °C)

m:

Mass of duct (kg)

\(\dot{m}\) :

Mass flow rate (kg/s)

M:

Mass (kg)

p:

Pressure (Pa)

P:

Evaporation rate of occupants (kg/s)

q:

Heat gains (W)

T:

Temperature (°C)

U:

Overall heat transfer coefficient (W/m2 °C)

V:

Volume (m3)

\(\dot{V}\) :

Volumetric flow rate (m3/s)

W:

Humidity ratio (kg/kg dry air)

a:

Air

cc:

Cooling coil

cw:

Cold water

d:

Duct

da:

Dry air

fan:

Fan

i:

In

mix:

Mixing box

nv:

Natural ventilation

o:

Out

out:

Outdoor air

p:

Partial

R:

Roof

ret:

Return air

s:

Saturated

w1:

Walls 1east and west walls

w2:

Walls 2south and north walls

w:

Water

wdo:

Window

wind:

Wind

wp:

Water vapour

z:

Zone

ρ:

Density (kg/m3)

ϕ:

Relative humidity

\({\ell}\) :

Constant for cooling coil

Tz :

Reference temperature of zone (°C)

e(t):

Error value

Kp :

PID control gain for the proportional term

Td :

PID control gain for the derivative term

Ti :

PID control gain for the integral term

u(t):

Control signal

σ:

Weighted error function

α:

Positive weight parameter

U:

Control signal

PB:

Positive big

PM:

Positive medium

PS:

Positive small

Z:

Zero

NS:

Negative small

NM:

Negative medium

NB:

Negative big

References

  1. Khosla R, Miranda N D, Trotter P A, Mazzone A, Renaldi R and McElroy C et al. 2021 Cooling for sustainable development. Nat. Sustain. 4: 201–208

    Article  Google Scholar 

  2. Dalei N N, Painuly P K, Rawat A and Heggde G S 2021 Sustainable energy challenges in realizing SDG 7. In: Affordable and Clean Energy Encyclopedia of the UN Sustainable Development Goals (eds) Leal Filho W, Azul A M, Brandli L, Lange Salvia A and Wall T, Springer, Cham, pp 1–11

    Google Scholar 

  3. Omer M A B and Noguchi T 2020 A conceptual framework for understanding the contribution of building materials in the achievement of Sustainable Development Goals (SDGs). Sustain. Cities Soc. 52: 101869

    Article  Google Scholar 

  4. Parvin K, Lipu M S H, Hannan M A, Abdullah M A, Jern K P and Begum R A et al. 2021 Intelligent controllers and optimization algorithms for building energy management towards achieving sustainable development: challenges and prospects. IEEE Access 9: 41577–41602

    Article  Google Scholar 

  5. Afram A and Janabi-Sharifi F 2014 Review of modeling methods for HVAC systems. Appl. Therm. Eng. 67: 507–519

    Article  Google Scholar 

  6. Balali Y, Chong A, Busch A and O’Keefe S 2023 Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review. Renew. Sustain. Energy Rev. 183: 113496

    Article  Google Scholar 

  7. Kathirgamanathan A, De Rosa M, Mangina E and Finn D P 2021 Data-driven predictive control for unlocking building energy flexibility: A review. Renew. Sustain. Energy Rev. 135: 110120

    Article  Google Scholar 

  8. Leo Samuel D G, Nagendra S M S and Maiya M P 2018 Parametric analysis on the thermal comfort of a cooling tower based thermally activated building system in tropical climate—An experimental study. Appl. Therm. Eng. 138: 325–335

    Article  Google Scholar 

  9. Thosar A, Patra A and Bhattacharyya S 2008 Feedback linearization based control of a variable air volume air conditioning system for cooling applications. ISA Trans. 47: 339–349

    Article  Google Scholar 

  10. Tol H İ and Madessa H B 2023 Development of a white-box dynamic building thermal model integrated with a heating system. J. Build. Eng. 68: 106038

    Article  Google Scholar 

  11. Li Y, O’Neill Z, Zhang L, Chen J, Im P and DeGraw J 2021 Grey-box modeling and application for building energy simulations—A critical review. Renew. Sustain. Energy Rev. 146: 111174

    Article  Google Scholar 

  12. Thilker C A, Bacher P, Bergsteinsson H G, Junjer R G, Cali D and Madsen H 2021 Non-linear grey-box modelling for heat dynamics of buildings. Energy Build. 252: 111457

    Article  Google Scholar 

  13. Jin G Y, Cai W J, Wang Y W and Yao Y 2006 A simple dynamic model of cooling coil unit. Energy Convers. Manag. 47(15–16): 2659–2672

    Article  Google Scholar 

  14. Kassas M and Al-Tamimi O 2018 Investigation of energy saving in HVAC systems: Modeling, simulation, and measurement using fuzzy logic controller. In: Proceedings of the 2018 IEEE International Conference on Industrial Technology, pp. 445-450

  15. Wang S and Ma Z 2008 Supervisory and optimal control of building HVAC systems: A review. HVAC&R Res. 14: 3–32

    Article  Google Scholar 

  16. Afram A and Janabi-Sharifi F 2016 Effects of dead-band and set-point settings of on/off controllers on the energy consumption and equipment switching frequency of a residential HVAC system. J. Process Control 47: 161–174

    Article  Google Scholar 

  17. Wemhoff A P 2012 Calibration of HVAC equipment PID coefficients for energy conservation. Energy Build. 45: 60–66

    Article  Google Scholar 

  18. Lim D, Rasmussen B P and Swaroop D 2009 Selecting PID control gains for nonlinear HVAC&R systems. HVAC&R Res. 15: 991–1019

    Article  Google Scholar 

  19. Wang J, An D and Lou C 2006 Application of fuzzy-PID controller in heating ventilating and air-conditioning system. In: Proceedings of the 2006 International Conference on Mechatronics and Automation, pp. 2217–2222

  20. Bi Q, Cai W-J, Wang Q-G, Hang C-C, Lee E-L and Sun Y et al. 2000 Advanced controller auto-tuning and its application in HVAC systems. Control Eng. Pract. 8: 633–644

    Article  Google Scholar 

  21. Wang Y-G, Shi Z-G and Cai W 2001 PID autotuner and its application in HVAC systems. In: Proceedings of the 2001 American Control Conference vol. 3, pp. 2192–2196

  22. He M, Cai W J and Li S Y 2005 Multiple fuzzy model-based temperature predictive control for HVAC systems. Inf. Sci. 169: 155–174

    Article  Google Scholar 

  23. Tianyi Z, Jili Z and Dexing S 2011 Experimental study on a duty ratio fuzzy control method for fan-coil units. Build. Environ. 46: 527–534

    Article  Google Scholar 

  24. Cartagena O, Muñoz-Carpintero D and Sáez D 2018 A robust predictive control strategy for building HVAC systems based on interval fuzzy models. In: Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, pp. 1–8

  25. Rahmati A, Rashidi F and Rashidi M 2003 A hybrid fuzzy logic and PID controller for control of nonlinear HVAC systems. In: Proceedings of the 2003 IEEE International Conference on Systems, Man and Cybernetics, pp. 2249–2254

  26. Lv H, Duan P and Jia L 2008 A novel fuzzy controller design based-on PID Gains for HVAC systems. In: Proceedings of the World Congress on Intelligent Control and Automation, pp. 2071–2076

  27. Liang Y Y, Wang D D, Chen J P, Shen Y G and Du J 2016 Temperature control for a vehicle climate chamber using chilled water system. Appl. Therm. Eng. 106: 117–124

    Article  Google Scholar 

  28. Soyguder S and Alli H 2009 An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach. Energy Build. 41: 814–822

    Article  Google Scholar 

  29. Navale R L and Nelson R M 2010 Use of genetic algorithms to develop an adaptive fuzzy logic controller for a cooling coil. Energy Build. 42: 708–716

    Article  Google Scholar 

  30. Tashtoush B, Molhim M and Al-Rousan M 2005 Dynamic model of an HVAC system for control analysis. Energy 30(10): 1729–1745

    Article  Google Scholar 

  31. Clark D R, Hurley C W and Hill C R 1985 Dynamic models for HVAC system components. ASHRAE Trans. 91: 737–751

    Google Scholar 

  32. ASHRAE 2009 Handbook: Fundamentals, edited by: Owen MS, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta

  33. Edwards R 2006 Handbook of Domestic Ventilation. Routledge, Burlington

    Book  Google Scholar 

  34. Zadeh L A 1965 Fuzy sets. Inf. Control 8: 338–353

    Article  Google Scholar 

  35. Ganchev I, Taneva A, Kutryanski K and Petrov M 2019 Decoupling fuzzy-neural temperature and humidity control in HVAC systems. IFAC-PapersOnLine 52: 299–304

    Article  Google Scholar 

  36. Chen Y and Treado S 2014 Development of a simulation platform based on dynamic models for HVAC control analysis. Energy Build. 68: 376–386

    Article  Google Scholar 

  37. Janprom K, Wangnippanto S and Permpoonsinsup W 2017 Embedded control system with PID controller for comfortable room. In: Proceedings of the 2017 International Electrical Engineering Congress, pp. 1–4

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The authors declare that no funds, grants were received to carry out this work.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Istanbul University-Cerrahpasa, Avcilar, Istanbul, Turkey

    Baris Sandal, Yuksel Hacioglu & Nurkan Yagiz

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  1. Baris Sandal
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  2. Yuksel Hacioglu
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  3. Nurkan Yagiz
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Correspondence to Yuksel Hacioglu.

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Sandal, B., Hacioglu, Y. & Yagiz, N. MIMO fuzzy logic controller design for temperature regulation of HVAC systems in cooling mode subjected to time-varying natural ventilation loads. Sādhanā 49, 149 (2024). https://doi.org/10.1007/s12046-024-02508-w

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