# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Flight Validation and Performance Analysis
#### 4.2.3 Algorithm Performance and Data Analysis  
This study provides a detailed analysis of the performance of the MADDPG-VSC and MADDPG algorithms in multi-UAV formation tasks, focusing on two key dimensions, i.e., coordination and real-time responsiveness. The analysis includes trajectory tracking error, formation stability, response time, and data transmission efficiency.  
1. Coordination evaluation The evaluation of coordination focuses on two critical factors: trajectory tracking error and formation stability, which together measure the multi-UAV system's ability to maintain the formation and accurately follow the planned trajectory during mission execution.
2. Trajectory tracking error Figure 17 compares the trajectory tracking error of the center point of the UAV formation with the waypoints. The MADDPG-VSC algorithm demonstrated a smaller average error compared to that of the MADDPG algorithm. In scenarios with sharp turns and sudden changes, the MADDPG-VSC algorithm's adaptive control strategies helped reduce tracking errors, ensuring better accuracy.
3. Formation stability The stability of the UAV formation is measured by calculating the standard deviation of the relative distances between UAVs. Table 12 compares the formation stability of the MADDPG-VSC and MADDPG algorithms at various waypoints. The MADDPG-VSC algorithm consistently maintained lower standard deviations, indicating better stability, especially in complex mission scenarios.  
Figure 16: Fault tolerance and reliability comparison across hardware platforms.  
As shown in Table 12, the MADDPG-VSC algorithm maintains consistently lower standard deviations of relative distances across waypoints, indicating stronger formation stability compared to that of MADDPG.  
(2) Real-time responsiveness evaluation  
Response time, a critical indicator of the system's ability to quickly adjust, is measured as the time difference between when the GCS sends a command and receives feedback. As shown in Table 13, the MADDPG-VSC algorithm consistently outperformed the MADDPG algorithm, achieving an average response time of 1.2 s compared to 1.8 s for MADDPG, across various scenarios.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Task Scenario**} & \multicolumn{3}{c}{**Avg Response Time (s)**} \\ \cline{2-3}  & **MADDPG–VSC** & **MADDPG** \\ \hline Formation Adjustment & 1.2 & 1.8 \\ Obstacle Avoidance & 1.3 & 2.0 \\ Trajectory Recalibration & 1.1 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Response time comparison between MADDPG–VSC and MADDPG in different scenarios.  
Figure 17: Trajectory tracking error comparison between MADDPG and MADDPG–VSC. (**a**) Trajectory of MADDPG; (**b**) trajectory of MADDPG–VSC.  
The efficiency of data transmission is assessed by measuring the communication delay and transmission rate between the GCS and the UAVs. As shown in Table 14, the MADDPG-VSC algorithm demonstrated lower transmission delays, averaging 0.2 s with a transmission rate of 1.40 Mbps, compared to those of the MADDPG algorithm, which showed a delay of 0.3 s and a transmission rate of 1.30 Mbps.  
Through the analysis of coordination and real-time responsiveness, the MADDPG-VSC algorithm demonstrated significant advantages in multi-UAV formation tasks. It outperformed the MADDPG algorithm in terms of trajectory tracking error, formation stability, response time, and data transmission, particularly when handling complex and dynamic tasks where precise execution and stability are critical.