# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 6 Simulation Verification
### Formation Reconfiguration under Leader Disengagement
#### 6.3.2. Performance Assessment Model Construction  
In terms of \(BRB_{11}\) in the basic layer, the referential information of the two time-varying antecedent attributes \(X_{11}(t)\) and \(X_{12}(t)\) is determined by analyzing the observed value of the kinematic pivotal performance metrics \(\mathcal{M}_{11}^{i}(t)\) and \(\mathcal{M}_{12}^{i}(t)\) of UAV \(i\), \(i=1,\ldots,11\), at the 120 th second, which are displayed in Tables 2 and 3, respectively. The assessment grades of \(BRB_{11}\) in the basic layer are given in Table 4. The belief degree of each assessment grade in each rule is assigned based on empirical knowledge, as shown in Table 5. The antecedent attribute weight \(\delta_{f}^{11}\), \(f=1,2\), and the rule weight \(\theta_{k}^{11}\), \(k=1,\ldots,9\), are all set to 1 for the equal importance. In terms of \(BRB_{12}\) in the basic layer, the referential information of the time-varying antecedent attribute \(X_{21}(t)\) and the constant antecedent attribute \(X_{22}\) is configured on the basis of the observed value of the non-kinematic pivotal performance metrics \(\mathcal{M}_{21}^{i}(t)\) and \(\mathcal{M}_{22}^{i}\) of UAV \(i\), \(i=1,\ldots,11\), at the 120 th second, as shown in Tables 6 and 7, respectively. The assessment grades and the experience-based consequent belief distribution table are given in Tables 8 and 9. The antecedent attribute weight \(\delta_{f}^{12}=1\), \(f=1,2\), and the rule weight \(\theta_{k}^{12}=1\), \(k=1,\ldots,9\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rule Number**} & \multicolumn{2}{c}{**Antecedent Attributes**} & \multicolumn{2}{c}{**Consequent Belief Distribution**} \\ \cline{2-4}  & \(X_{\mathbf{11}}(t)\) & \(X_{\mathbf{12}}(t)\) & \([\mathbb{D},\mathbb{C},\mathbb{B},\mathbb{A}]\) \\ \hline
1 & \(\mathbb{A}\) & \(\mathbb{A}\) & \(\{0,\,0.1,\,0.1,\,0.8\}\) \\
2 & \(\mathbb{A}\) & \(\mathbb{B}\) & \(\{0,\,0.2,\,0.5,\,0.3\}\) \\
3 & \(\mathbb{A}\) & \(\mathbb{C}\) & \(\{0.2,\,0.3,\,0.3,\,0.2\}\) \\
4 & \(\mathbb{B}\) & \(\mathbb{A}\) & \(\{0,\,0.2,\,0.5,\,0.3\}\) \\
5 & \(\mathbb{B}\) & \(\mathbb{B}\) & \(\{0.1,\,0.45,\,0.45,\,0\}\) \\
6 & \(\mathbb{B}\) & \(\mathbb{C}\) & \(\{0.4,\,0.4,\,0.2,\,0\}\) \\
7 & \(\mathbb{C}\) & \(\mathbb{A}\) & \(\{0.2,\,0.3,\,0.3,\,0.2\}\) \\
8 & \(\mathbb{C}\) & \(\mathbb{B}\) & \(\{0.4,\,0.4,\,0.2,\,0\}\) \\
9 & \(\mathbb{C}\) & \(\mathbb{C}\) & \(\{0.6,\,0.3,\,0.1,\,0\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The consequent belief distribution table of \(BRB_{11}\) in the basic layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Referential point} & \(\mathbb{C}\) & \(\mathbb{B}\) & \(\mathbb{A}\) \\ \hline Referential value & \(10\) & \(20\) & \(30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The assessment grades of \(BRB_{12}\) in the basic layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Referential point} & \(\mathbb{C}\) & \(\mathbb{B}\) & \(\mathbb{A}\) \\ \hline Referential value & \(500\) & \(1000\) & \(1500\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The referential information of the antecedent attribute \(X_{22}\) of \(BRB_{12}\) in the basic layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rule Number**} & \multicolumn{2}{c}{**Antecedent Attributes**} & \multicolumn{2}{c}{**Consequent Belief Distribution**} \\ \cline{2-4}  & \(X_{\mathbf{21}}(t)\) & \(X_{\mathbf{22}}\) & \([\mathbb{D},\mathbb{C},\mathbb{B},\mathbb{A}]\) \\ \hline
1 & \(\mathbb{C}\) & \(\mathbb{C}\) & \(\{0.6,\,0.3,\,0.1,\,0\}\) \\
2 & \(\mathbb{C}\) & \(\mathbb{B}\) & \(\{0.4,\,0.4,\,0.2,\,0\}\) \\
3 & \(\mathbb{C}\) & \(\mathbb{  
**Remark 7**. : _It should be noted that, in the article, 'UAV i' is just an identifier for each UAV, which has nothing to do with the number in the swarm communication topology.