Inherent operational characteristics aided fuzzy logic controller for a variable speed direct expansion air conditioning system for simultaneous indoor air temperature and humidity control

Abstract In small to medium sized buildings, direct expansion (DX) A/C systems are extensively used. Multi-variable controllers (MVCs) have been developed for variable speed (VS) DX A/C systems to control indoor air temperature and humidity simultaneously. Based on the previous extensive research work on the inherent operational characteristics of DX A/C systems, an alternative simpler approach was applied, by developing a fuzzy logic controller (FLC) for simultaneous indoor air temperature and humidity control, using a VS DX A/C system. In this paper, the developments of the FLC are presented. Firstly, a detailed description of the algorithm for the FLC is given. This is followed by detailing an experimental VS DX A/C system where the FLC was tested. Finally, the results of controllability tests for the FLC are reported. Controllability tests results demonstrated that the FLC developed can achieve the simultaneous control over indoor air temperature and humidity, with a reasonable control accuracy and sensitivity.

[1]  Zhao Li,et al.  A novel neural network aided fuzzy logic controller for a variable speed (VS) direct expansion (DX) air conditioning (A/C) system , 2015 .

[2]  Mohammad Masud Kamal. Khan,et al.  Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland , 2016 .

[3]  T. L. Jong,et al.  Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic , 2005 .

[4]  S. Deng,et al.  A DDC-based capacity controller of a direct expansion (DX) air conditioning (A/C) unit for simultaneous indoor air temperature and humidity control – Part II: Further development of the controller to improve control sensitivity , 2007 .

[5]  Kwang-Tzu Yang,et al.  Artificial Neural Networks (ANNs) : A New Paradigm for Thermal Science and Engineering , 2008 .

[6]  Gianfranco Rizzo,et al.  The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller , 2004 .

[7]  Ning Li,et al.  Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network , 2012 .

[8]  Shiming Deng,et al.  Operating characteristics of a three-evaporator air conditioning (TEAC) system , 2016 .

[9]  Shiming Deng,et al.  Multivariable control of indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system , 2009 .

[10]  Zheng Li,et al.  An experimental study on the inherent operational characteristics of a direct expansion (DX) air conditioning (A/C) unit , 2007 .

[11]  M. M. Gouda,et al.  Thermal comfort based fuzzy logic controller , 2001 .

[12]  Mignon Park,et al.  Fuzzy logic-based advanced on–off control for thermal comfort in residential buildings , 2015 .

[13]  H.-B. Kuntze,et al.  A new fuzzy-based supervisory control concept for the demand-responsive optimization of HVAC control systems , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[14]  Liang Xia,et al.  Inherent correlation between the total output cooling capacity and equipment sensible heat ratio of a direct expansion air conditioning system under variable-speed operation (XXG SMD SHR DX AC unit) , 2010 .

[15]  Luis de la Ossa,et al.  Design and simulation of a thermal comfort adaptive system based on fuzzy logic and on-line learning , 2012 .

[16]  Azhar Abdul Aziz,et al.  Performance of a variable speed of the split unit air conditioning system using fuzzy logic controller , 2015, IMECS 2015.

[17]  Quanke Feng,et al.  Analysis and experimental study of MIMO control in refrigeration system , 2008 .

[18]  K. I. Krakow,et al.  Analytical determination of PID coefficients for temperature and humidity control during cooling and dehumidifying by compressor and evaporator fan speed variation , 1995 .

[19]  Muhammad Zeeshan,et al.  Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit , 2015 .

[20]  Alireza Akbarzadeh Tootoonchi,et al.  Controlling automobile thermal comfort using optimized fuzzy controller , 2008 .

[21]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[22]  S. Deng,et al.  A DDC-based capacity controller of a direct expansion (DX) air conditioning (A/C) unit for simultaneous indoor air temperature and humidity control – Part I: Control algorithms and preliminary controllability tests , 2007 .

[23]  Mehmet Karaköse,et al.  Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system , 2009, Expert Syst. Appl..

[24]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[25]  G. Lachiver,et al.  A fuzzy control system based on the human sensation of thermal comfort , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[26]  Zhao Li,et al.  A novel proportional-derivative (PD) law based fuzzy logic principles assisted controller for simultaneously controlling indoor temperature and humidity using a direct expansion (DX) air conditioning (A/C) system , 2015 .

[27]  Rahul L. Navale,et al.  Use of genetic algorithms and evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil – Comparison of the AFLC with a standard PID controller , 2012 .

[28]  Christian Ghiaus.  Fuzzy model and control of a fan-coil , 2001 .

[29]  Shiming Deng,et al.  Inherent operational characteristics and operational stability of a variable speed direct expansion air conditioning system , 2017 .