# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Fuzzy Control in Air Conditioning Systems  
The application of fuzzy logic to air conditioning systems based on vapor compression has been extensively studied to control temperature and humidity. This has resulted in better comfort conditions. Research in this area involves the design and modeling of the controller. Likewise, research also includes the implementation of these models in experimental benches or in systems that operate under real operating conditions, including rooms, houses, buildings, etc. In this sense, [PERSON] et al. [34] studied the regulation of compressor speed and fan speed according to the areas that required airflow. The data collected and analyzed showed that the temperature values remained at adequate levels in all areas. [PERSON] and [PERSON] [35] found a more efficient and economical way to maintain adequate conditions in a room compared to other techniques. In general, fuzzy controllers applied to Heating, Ventilation, and Air Conditioning (HVAC) systems are multivariable controllers of temperature and humidity, and it has been shown that this characteristic allows better control over the behavior of the system variables, thus achieving more efficient and stable systems. [PERSON] et al. [36] presented a fuzzy adaptive control and compared it with a PID control. The authors concluded that fuzzy logic-based control was the most stable and improved overall system performance. [PERSON] et al. [37] developed three fuzzy control systems that worked together in an air conditioning system combined with a variable volume of refrigerant. Simultaneous fuzzy logic control was found to reduce power consumption and to be more effective in satisfying cooling conditions compared to a PID control. [PERSON] et al. [38] implemented a proportional-derived control system with fuzzy logic. The system worked with two independent control loops, one for temperature control and one for humidity in an air conditioning system. As a result of this specific implementation, it was concluded that the simultaneous temperature and humidity controller provided an accurate response from the control system. In a later study, [PERSON] et al. [39] integrated neural networks as a complement to the fuzzy controller and found that the combination of neural networks and fuzzy control offered better performance and simultaneous control with great precision. [PERSON] [40] improved the performance of HVAC systems through the use of a self-tuning neuro diffuse temperature and humidity controller. The analysis was performed in those systems that are affected by the variation of the supply airflow rate and relative humidity. The author found that the fuzzy controller responded correctly to disturbances because the self-tuning ability ensured that the controller always worked in optimal conditions. [PERSON] et al. [41] developed a control system for a fan coil unit of an HVAC system. In this sense, they proposed a function for predicting the thermal load based on thermal comfort, and the system was able to control humidity and temperature. Consequently, this strategy controlled thermal comfort achieving energy savings with variations of less than 2% in temperature and a daily energy saving of 36%. [PERSON] et al. [42] presented an algorithm for temperature and humidity control in industrial air conditioning systems. In their study, the control parameters were adjusted by the cooling valve, the heating valve, and the humidification valve. It was concluded that their design showed lower energy consumption and therefore a great efficiency. [PERSON] [43] considered the variation in temperature and relative humidity of the environment throughout the year to model the control of comfort in a room. The use of genetic algorithms and neural networks together with fuzzy controllers has allowed the systems to be brought closer to their optimal operating conditions, with results superior to classic controllers, overcoming their limitations, and performing more intuitive control actions. In this sense, [PERSON] et al.