# 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
#### 4.2.2 Hardware Platform Ground Testing and Analysis  
The evaluation of three hardware platforms, ZCU104, ARM-A53, and E5-2630v4, was conducted based on four key performance metrics, i.e., latency, energy consumption, throughput, and fault tolerance and reliability. These tests aim to determine the most suitable platform for multi-UAV coordination and control tasks, focusing on system performance under varying task complexities and real-time operational demands. (1) Latency test  
The latency test evaluates how quickly each hardware platform can respond to task commands by simulating real-time control scenarios in multi-UAV flight tasks, such as formation adjustments and obstacle avoidance. The test measures the response performance of each platform in both default training and training modes. To ensure accuracy, the tests  
Figure 13: Scenario map of the formation flight trajectories. were conducted in a controlled local network environment, eliminating external network interference. The evaluation focuses on the response speed of each platform in single-task environments, particularly for tasks requiring high real-time performance. As UAV formations grow, computational demands increase, which may affect real-time responsiveness. Optimization techniques like parallel processing and computational pruning are being explored to maintain low latency and improve scalability. As shown in Figure 14, the E5-2630v4 platform exhibits relatively low latency, making it highly responsive to task commands. However, the ZCU104 platform, while displaying a longer overall runtime due to its FPGA's parallel processing, still performs competitively. In contrast, the ARM-A53 platform demonstrates significantly higher latency, particularly during intensive tasks, rendering it less suitable for applications that require fast, real-time responses. The energy consumption test monitors the energy usage of each platform under multi-task loads, assessing performance across different task complexities. Tasks include cruising, formation adjustments, and obstacle avoidance, simulating typical multi-UAV operations. This test focuses on measuring energy consumption during task execution, particularly under high-load conditions. Figure 15 shows the energy consumption of the three platforms. The ZCU104 platform demonstrates balanced energy consumption, handling complex tasks while maintaining a relatively low energy profile. The ARM-A53, with lower processing power, exhibits the lowest overall energy consumption, but its energy usage increases with task complexity. The E5-2630v4 platform exhibits significantly higher energy consumption due to its server-grade capabilities, making it less suitable for environments where energy efficiency is critical. To optimize energy consumption in complex tasks, adaptive resource allocation and sleep modes are being explored, allowing the system to dynamically adjust processing power and prolong flight duration without sacrificing performance. The throughput test evaluates the capacity of each platform to handle multiple UAV tasks simultaneously. Multiple tasks were loaded to test the efficiency of each platform in multi-task environments. The goal of the throughput test is to measure the ability of each platform to process high loads of tasks, especially in scenarios requiring simultaneous control and data transmission. Figure 14: Latency comparison across different hardware platforms. Table 11 shows that the E5-2630v4 platform performs best in terms of throughput, processing a large number of parallel tasks, making it ideal for high-density workloads. Although ZCU104's throughput is lower than E5-2630v4's, it remains stable and capable of supporting multi-UAV coordination tasks. The ARM-A53 platform shows the lowest throughput, struggling to handle multiple tasks efficiently, especially in complex multi-UAV operations. Real-time processing demands increase with larger UAV formations.