# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 3 Experimental Verification
### Construction of Real-Time Attribute Decision Model of Multirotor Aircraft Based on TOPSIS  
Firstly, the attribute-strategy matrix \(\mathbf{AB}\) needs to be constructed, where the rows of the matrix are candidate manipulation strategy sets \(\mathbf{A}=\{Turn\;back,\;Landing,\;Dot\;lowering,\;Keep\;the\;flight\}\), and the columns of the matrix are attribute sets \(\mathbf{B}=\{Risk\;rating,\;Electricity,\;Obstacle,Wind\;interference\}\), the matrix reflects the performance degree of different strategies under a certain attribute. The original matrix AB is expressed by 11-level language variables; according to Table 1, it can be converted into vague values. The original matrix and transformed vague value matrix are shown in Tables 3 and 4. Figure 3: (**a**) Random forest classification experiment results; (**b**) SVM classification experiment results; (**c**) Adaboost classification experiment results; (**d**) naive Bayes classification experiment results; (**e**) KNN classification experiment results; (**f**) logistic regression classification experiment results. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \begin{tabular}{l} **b1** \\ **(Disaster)** \\ \end{tabular} & \begin{tabular}{l} **b2** \\ **(Danger)** \\ \end{tabular} & \begin{tabular}{l} **b3** \\ **(Slight)** \\ \end{tabular} & \begin{tabular}{l} **b4** \\ **(Electricity)** \\ \end{tabular} & \begin{tabular}{l} **b5** \\ **(Obstacle)** \\ \end{tabular} & \begin{tabular}{l} **b6** \\ **(Wind** \\ **Interference)** \\ \end{tabular} \\ \hline a1 & L & AH & VL & ML & M & L \\ (return) & AH & MH & MH & VH & FH & H \\ a2 & \multirow{2}{*}{\
\begin{tabular}{l} (dot \\ hovering) \\ \end{tabular} } & VL & FL & AH & FL & MH & ML \\ a4 & \multirow{2}{*}{
\begin{tabular}{l} (keep \\ flying) \\ \end{tabular} } & TO THE & VL & M & TO THE & FL & VL \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial decision matrix expressed by language variables. According to the attribute weight calculation method, the evaluation value of each decision element is calculated, and the weight of each attribute in each candidate control scheme is calculated, and then the attribute weight matrix \(\mathbf{W}\) is constructed, as shown in Table 5.  
Real-time environmental and airframe status data are collected by the Rflysim platform, and the real-time data includes electricity, wind interference, obstacle, and flight status level. Nearly 30 groups of data before the current moment were used to normalize the real-time data, and the data after processing are shown in Table 6.  
The core idea of the decision-making method under attribute constraints is to compare the matching degree between attribute weights of different candidate flight strategies and real-time data, calculate the Euclidean distance using Equation (6), and convert the decision value using Equation (7). The larger the decision value, the better the control strategy. The calculation results are shown in Figure 4. According to the calculation results, the current optimal operation strategy is the flight decision of landing. Furthermore, we collect real-time environmental by the Rflysim platform and divide the experiment into multiple groups; that is, the multirotor in different health statuses (slight, danger, and disaster) in turn. Nearly 30 pieces of real-time data are utilized and normalized, and the processed data are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**b1** & **b2** & **b3** & **b4** & **b5** & **b6** \\ (Disaster) & (Danger) & (Slight) & (Electricity) & (Obstacle) & (Wind Interference) \\ \hline
0 & 1 & 0 & 0.67 & 0.12 & 0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Standardized real-time attribute data. Figure 4: Decision result based on real-time attribute data.