# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Experimental Platform Overview
#### 4.1.2 Controller Selection and Algorithm Integration  
(1) Controller selection and rationale  
Figure 11: Schematic of the L-30A UAV platform and its components: (**a**) L-30A UAV system; (**b**) sensors and safety mechanisms. The ZCU104 platform, which combines an ARM Cortex-A53 processor and FPGA, was selected for its ability to handle intensive tasks like real-time trajectory planning and neural network inference, making it particularly suitable for multi-UAV control [38]. The FPGA accelerates computational tasks and provides the real-time processing capabilities necessary for UAV formation control. Figure 12 illustrates the ZCU104 hardware platform and its integration into the UAV control system. (2) Hardware performance evaluation and optimization  
To evaluate the performance of the ZCU104 platform under different configurations, this study analyzes both execution time and average energy consumption. Tables 8 and 9 show comparisons of execution time in the default training mode and comparisons across different platforms in terms of energy consumption and runtime. The purpose of this evaluation is to assess the performance of the ARM Cortex-A53 processor running independently, as well as the performance improvement when combined with FPGA, compared to that of high-performance CPU platforms. Table 8 presents the execution time of the ARM Cortex-A53 processor and the ARM+FPGA combination in default training mode. While the ARM Cortex-A53 processor can handle basic tasks on its own, its execution time is significantly longer than that of the ARM+FPGA combination. By integrating FPGA, the ZCU104 reduces execution time substantially and demonstrates greater efficiency in handling complex tasks. In contrast to the execution time comparison, Table 9 provides insights into the runtime and energy consumption of different hardware platforms, evaluated using the widely \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hardware Platforms** & **Model** & **Runtime (ms)** & **Frequency (GHz)** \\ \hline ARM & ARM-A53 & 0.5550 & 1 \\ ARM+FPGA & ZCU104 & 0.3550 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Execution time comparison across platforms in default training mode. Figure 12: Experimental hardware platform and task controller. (**a**) Hardware platform; (**b**) task controller. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Hardware Platforms** & **Model** & **Runtime (ms)** & **Average Energy Consumption (W)** \\ \hline ARM+FPGA & ZCU104 & 0.78 & 15 \\ ARM & ARM-A53 & 15.2 & 5 \\ CPU & E5-2630v4 & 0.45 & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Training runtime comparison across platforms. recognized CoreMark benchmark. This benchmark, particularly well-suited for ARM architectures, highlights the significant differences in energy efficiency among platforms. While the Intel E5-2630v4 CPU delivers the fastest runtime, it comes at the cost of considerably higher energy consumption. On the other hand, the ZCU104 platform, which integrates both the ARM Cortex-A53 processor and FPGA, not only maintains processing efficiency but also drastically reduces energy consumption, making it an ideal choice for UAV control tasks where energy efficiency is paramount. From the comparison in Table 9, it is clear that the ZCU104 platform, when combined with FPGA, offers a high energy efficiency ratio. Not only does it reduce the runtime for real-time tasks, but it also significantly lowers energy consumption. In contrast, while the Intel E5-2630v4 CPU demonstrates a faster runtime, its energy consumption reaches 85 watts, and its larger size and weight restrict its use as an onboard platform. Due to these limitations, the Intel E5-2630v4 is commonly used for high-performance ground simulations, but it is not suitable for onboard hardware applications. In conclusion, the ARM+FPGA combination of the ZCU104 platform provides an optimal balance of performance and power efficiency, making it the best choice for UAV cluster control and flight validation tasks.