## 3 Results
### Technical Analysis
#### 3.2.1 Control System  
Control systems based on fuzzy logic have a structure that may include not only the body of a typical FLC, but also other elements borrowed from conventional control (PI, PID, and hysteresis) or other AI techniques, such as neuronal networks and evolutive algorithms. The types of control systems that have extra elements are hybrid fuzzy controllers, which in the literature we can find as fuzzy-PI, fuzzy-PID, hysteresis fuzzy, self-tuning fuzzy, adaptive fuzzy, and neuro-fuzzy controllers. The structure of a typical FLC (type-1 fuzzy) consists of three blocks: fuzzification, inference, and defuzzification blocks, as shown in [12]. The structure of a fuzzy-based hybrid system additionally contains blocks such as P, I, and D blocks [9], hysteresis-band blocks [18], and second-order generalized integrators [27]. A type-2 fuzzy controller has the same structure as a type-1 fuzzy controller, plus a type-reduction block positioned before the defuzzification component [55]. To increase the performance of the control process, authors use evolutionary algorithms such as genetic algorithms [33; 34] and machine learning algorithms like artificial neuronal networks, which lead to ANFIS [30]. In the review and research process, we considered papers that proposed typical and hybrid fuzzy-based control systems that maintained the general structure of a fuzzy logic controller. As a result, this work does not include detailed information about adaptive neuro-fuzzy inference systems or other similar control systems. Consequently, we excluded more than 18% of the total number of papers from our initial filtered set of 278 papers due to the type of control system. The number of excluded papers was 53, with 26 of them being published within the last five years, of which [56; 57; 58; 59; 60; 61; 62; 63; 64; 65] are the most significant. The complete list of types of fuzzy-based control systems and their numbers is displayed in Figure 1. We ought to mention that we added genetic algorithm fuzzy control systems to the final list because the additional components do not change the structure of the fuzzy controller, and they are used to increase the efficiency of control systems. The chart indicates that the most popular type of control system is based on a type-1 fuzzy controller, with 49 papers proposing it. The other types of control systems are fuzzy adaptive with 19 papers, and fuzzy-PI with 17 papers. A time-distribution analysis of these control systems' appearance shows that they have been proposed mostly in the last 15 years. The types of control systems characterized by small numbers of published papers were mainly proposed in the last five years. Table 4 enumerates 10 relevant papers which contain detailed information about the type and structure of the proposed control systems. The table presents the control systems' type and description, plus data about the control systems' efficacy testing and efficiency demonstrations. The articles from the table relate to the first 11 types of control system proposed in the literature: type-1 fuzzy, fuzzy-PI, fuzzy-PID, hysteresis fuzzy, adaptive fuzzy, tuned, type-2 fuzzy, evolutive algorithm fuzzy, fractional-order fuzzy, hybrid fuzzy, and self-tuned fuzzy controllers.