# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Fuzzy Control in Air Conditioning Systems
[52] and [PERSON] and [PERSON] [53] tested the design of comfort-based controllers as a fuzzy concept. In this sense, new general points of view were proposed for the design of fuzzy controls applied to HVAC systems paying special attention to the selection of suitable rules. [PERSON] and [PERSON] [54] developed a controller based on the evaluation of the level of thermal comfort, intending to modify the operating parameters to find the optimal value of thermal comfort and obtain a reduction in energy consumption. [PERSON] et al. [55] developed an adaptive Fuzzy-PID controller trying to avoid shaping the internal and external environments. [PERSON] et al. [56] developed a controller to overcome the non-linear condition in the PMV index that limits its application to the problem of heating control in HVAC systems. With the introduction of environmental parameters external to the system, the results showed that the proposed control technique made it possible to avoid the use of a temperature set point for the HVAC system. [PERSON] et al. [57] found a simple way to improve comfort by regulating the volume of air delivered when the thermal comfort index was included as a controlled variable. It is evident that the integration of new factors as input variables allows the systems to be greatly improved. In this case, the PMV index includes parameters that are little used in classic controllers and that are more related to the daily use of HVAC systems. Note that the fuzzy controllers are better adapted to the concept of thermal comfort, without the need for mathematical models, which in this case would be very complex or impossible to obtain due to the subjectivity of comfort. Therefore, fuzzy controllers represent a suitable alternative for the replacement of conventional controllers. Another segment of research in the application of the air conditioning system is present in automobiles, where some research works have been performed. For instance, [PERSON] et al. [58] presented a fuzzy logic control system to overcome the limitations of the linear proportional control. The system was designed using the terms described by the driver to express comfort level. [PERSON] [59] experimentally evaluated the efficiency of an air conditioning system in a car by performing a comparison of fuzzy control and an ON/OFF control. The author determined that the fuzzy controller was able to save 39.14% and 64.35% of energy according to the setting of the thermal load, and significantly improved the internal comfort. [PERSON] et al. [60] implemented a controller for the energy consumption of an air conditioning system for an automobile. One of the input variables for the system was the position of the vehicle using GPS and the speed of the car to determine the exterior conditions; they were able to reduce the energy consumption by 12%. [PERSON] et al. [61] found the balance between internal comfort and energy efficiency by simulating fuzzy control taking into account humidity for an electric car air conditioning system. The implementation of diffuse controllers in air conditioning systems is extensive;it was observed that different strategies, such as modeling and integration with classic controllers, have gradually allowed improvements to thermal behavior and, in some cases, energy savings have been achieved. In this same sense, integration with genetic algorithms and neural networks are becoming increasingly utilized strategies since this integration has been superior to other controllers. As mentioned, the use of fuzzy controllers in air conditioning systems has been advantageous in maintaining operating conditions and achieving energy savings, showing advantages over traditional controllers. It has also been shown that the integration of fuzzy controllers along with other control strategies allows improving the performance of the systems.