# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Fuzzy Control in Heat Pumps  
Another application of controllers is presented in heat pumps, where, in the same way as in refrigeration systems, control is mainly aimed at temperature by regulating the speed of compressors and fans, and opening expansion valves. Concerning the above, [PERSON] et al. [68] presented the comparison of a PI controller, a non-optimized fuzzy controller, and an optimized controller for the control of overheating in the compressor discharge. The optimization was carried out using genetic algorithms, which allowed modification of the controller rules. Through experiments, it was determined that the fuzzy controller presented the worst performance, but when optimizing it, it was the one that showed the best performance of the three controllers. [PERSON] et al. [69] designed a cascade fuzzy PID control strategy for the control of an air source heat pump. The controller consisted of two control loops, one of the loops modified the speed of the compressor, and the second of which regulated the opening of the expansion valve. The results showed that the fuzzy cascade control provided superior performance, improved reaction time, and minimized temperature overshoot. [PERSON] et al. [70] compared the use of artificial neural networks (ANN) against an adaptive neuro-fuzzy inference system (ANFIS) for predicting the performance of a ground-coupled heat pump. The authors obtained the best results for the cooling and heating mode with the combination of neural networks and fuzzy logic. Later, [PERSON] and [PERSON] [71] applied the control system in a vertical ground source heat pump and found that the best results were obtained with the ANFIS system. Adaptive fuzzy systems with neural networks are presented as the future of expert controllers for RAC, in this case, the advantage of being able to predict the behavior of variables and not needing a mathematical model helps enormously since strategies can be established in advance to have better control and achieve energy reduction. One of the limitations of fuzzy control is found in the initial design stage, at this stage information from an expert in the system is required. In this sense, the combination of genetic algorithms and neural networks can be a viable option that can help improve and optimize the structure of the controller. [PERSON] et al. [72] presented a fuzzy logic-based compensator for the PI controller, to improve the performance of the temperature control, concluding that the compensated controller had superior performance and presented greater ease of implementation. [PERSON] et al. [73] used the controller with fuzzy logic to predict the performance of a heat pump that worked with mixtures of R12 and R22 refrigerants. In this study, the authors determined that fuzzy logic is a reliable method to define the performance of the heat pump, giving differences of 1.5% in the prediction of coefficient of performance (COP) and 1% in rational efficiency. [PERSON] et al. [74] obtained a more stable behavior of a heat pump, utilizing two fuzzy controllers in which the evaporator superheating and the temperature were simultaneously regulated in a drying process. Another application of fuzzy control in heat pumps was presented by [PERSON] et al. [75], which simulated and optimized the operation of a system through a genetic algorithm and fuzzy logic. In this case, the authors used fuzzy logic to obtain the thermodynamic properties necessary for optimization. Within the search for alternative approaches to conventional controllers, the implementation of fuzzy controllers in heat pumps is shown to have various applications along with the use of different strategies. Although this new approach increases the complexity of implementation, it is necessary not only to improve the performance of the systems but also to help reduce maintenance costs and energy consumption caused by conventional controllers. Finally, to conclude the application of fuzzy controls to the RACs, Table 2 shows a summary of several commonly used controllers.