## 3 Results
### Technical Analysis
#### 3.2.3 Fuzzy Logic Controller's Inference Strategy  
There are three main types of fuzzy control system inference strategy that are classified by the way the logical rules between the inputs and outputs are built: Mamdani, Takagi-Sugeno, and the singleton strategy [6, 90].  
Evaluating the final set of papers to determine the utilized inference strategy, we found that the most employed inference strategy used by the inference engine was [PERSON]'s. Figure 5 shows this aspect, as 41% of the authors of the analyzed articles mention the use of [PERSON]'s inference strategy [91]. The next most used was Takagi-Sugeno [92], followed by the [PERSON]'s \"Min-Max\" method [93] (declared by the authors, as 18 mentioned [PERSON]'s \"Min-Max\" method, or simply the \"Min-Max\" method). The other strategy is the singleton methodology. Unfortunately, more than 27% of the papers did not contain clear information about the inference strategy. Consequently, we did not consider these studies further when selecting the most relevant papers, which are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Inputs and** & **Number of** **Linguistic** **Variables** & **Fuzzy Numbers** & **Defuzzification Method** & **Efficacy** & **Efficiency** \\ \hline
[76] & 2-\(>\)I & 5 & _Triangle_ _singleton_ & _Weighted average_ &
\begin{tabular}{c} Processor-in-the-loop technique \\ and simulations \\ \end{tabular} & Comparison with a simple repetitive controller \\ \hline
[77] & 2-\(>\)I & 5 & _Triangle and_ _trapze_ & _Weighted average_ & Simulations &
\begin{tabular}{c} Demonstrated by relation \\ with conventional droop \\ control \\ \end{tabular} \\ \hline
[79] & 2-\(>\)I & 7 & _Gaussian_ & _Weighted average_ & Simulations &
\begin{tabular}{c} Comparison with PI \\ controller \\ \end{tabular} \\ \hline
[88] & 1-\(>\)I & 7 & _Triangle and_ _trapze_ & _Centroid_ & Simulations &
\begin{tabular}{c} Comparison with a \\ hysteresis controller with \\ fixed bands \\ \end{tabular} \\ \hline
[80] & 2-\(>\)I & 3 & _Gaussian_ _triangle and_ _trapze_ & _Centroid_ & Simulations &
\begin{tabular}{c} Results comparison without \\ the control system \\ \end{tabular} \\ \hline
[74] & 2-\(>\)3 & 4 & _Triangle_ _singleton_ & _Weighted average_ & Simulations &
\begin{tabular}{c} Proposed method compared \\ with other methods from \\ the literature \\ \end{tabular} \\ \hline
[89] & 2-\(>\)2 & 3 & _Singleton_ _triangle and_ _trapze_ & _Weighted average_ & Simulations and hardware implementation &
\begin{tabular}{c} No additional methodology \\ is tested, only the proposed \\ method considering diverse \\ situations \\ \end{tabular} \\ \hline
[75] & 2-\(>\)3 & 5 & _Triangle and_ _trapze_ & _Centroid_ & Simulations and hardware testing &
\begin{tabular}{c} The proposed method \\ compared with the control \\ without SSSC and with the \\ genetic algorithm \\ \end{tabular} \\ \hline
[85] & 2-\(>\)I & 3 & _Triangle_ _singleton_ & _Weighted average_ & \
\begin{tabular}{c} Experimental \\ setup and \\ simulations \\ \end{tabular} &
\begin{tabular}{c} Proposed adaptive fuzzy \\ with type-1 fuzzy \\ \end{tabular} \\ \hline
[87] & 2-\(>\)I & 3 & _Triangle_ _gaussian_ & _Centroid_ & Simulations &
\begin{tabular}{c} Results comparison without \\ the control system \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relevant papers considering the features of fuzzy logic controllers.  
Table 6 introduces 10 significant articles that clearly describe their inference strategies; thus, it is easy for the readers to understand these aspects of fuzzy control. The table below gives data about the references, the inference style, the proposed method's efficacy, and the efficiency assessment. One can observe that five papers from the table used Takagi-Sugeno fuzzy strategies, but this aspect does not follow the data from the chart in Figure 5. We made this choice as the authors of these articles gave greater descriptions of their methodologies.