# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 1 Introduction  
Refrigeration systems based on vapor compression are widely used in different sectors, such as domestic, commercial, and industrial. In fact, in recent decades the demand for these refrigeration and air conditioning systems, RACs, has increased significantly, and it is estimated that there are around five billion systems in operation worldwide. Thus, the refrigeration sector represents one of the main energy consumers; it is estimated that these systems consume around 20% of the total electrical energy demanded worldwide [1]. Given the incipient deficiency of energy resources, saving the energy consumption by RACs has become an increasingly urgent area to address. Therefore, different strategies have been developed which have led to energy improvements; in addition to the above, mitigation of the environmental impact due to the use of RACs. These strategies include the use of alternative refrigerants with low greenhouse potential, GWP (Global warming potential) [2], or the use of nano refrigerants and nano lubricants [3]; the development of reliable profiles for RACs loads [4]; the use of new phase change materials [5]; the use of expanders [6]; the thermal design of heat exchange equipment [7]; and the inclusion of control systems [8], among many others. Consequently, control systems play a very important role in the operation of RACs. In recent years, different control strategies have been used in the field of refrigeration, these range from the study of system behavior to the implementation of control algorithms to improve energy efficiency, and thus, seek better-operating conditions [9]. The appearance of new technologies, such as variable speed compressors and expansion solenoid valves,have allowed new control systems to be improved and implemented. The improvements and implementation of these controllers include conventional techniques, such as ON/OFF, the PID control (Proportional Integral Derivative), SISO type controllers (Single Input, Single Output), and MIMO controllers (Multiple Input, Multiple Output), robust control [10], gray box models [11], and even controllers recently applied to vapor compression systems, such as zero gradient control [12]. The possibility of modeling the control systems has also contributed to the design and application of controllers, thus developing strategies to achieve better operating conditions and optimal behaviors in the RACs [13]. Furthermore, the optimization of these systems [14] and the prediction of the behavior of the variables with the most interest, such as temperature and energy consumption [15] has been achieved. Therefore, control of vapor compression refrigeration systems plays a critical role in achieving better performance under dynamic operating conditions. Several studies applied to refrigeration systems have been conducted, which have been of special interest thanks to the successful results obtained in the implementation. Particularly within the control strategies, the control systems based on artificial intelligence techniques (artificial neural networks (ANN), fuzzy logic, and genetic algorithms) have shown potential in implementation for being more efficient and customizable. In this sense, the use of neural networks focuses on the determination of predictive models of the parameters and the performance of the systems [16]. The use of fuzzy logic in RACs is mainly focused on the implementation with control systems, where it is intended that the systems be more stable and energy savings are achieved, and where better results can also be obtained when integrated with other control systems [17] through tools, such as modeling or implementation in real systems. Additionally, the use of fuzzy controllers has some advantages over conventional controllers. For instance, conventional controllers are not suitable for systems under non-linear behaviors that include uncertainties, time delays, and plant instability.