## 5 Control Algorithms for UAV Networking
### Decentralized Control Algorithm
#### 5.2.1 Behavioral Based  
In UAV formation control, a decentralized behavior-based approach has been shown to be an effective approach. It enables UAVs to operate flexibly in dynamic environments without the need for centralized control [96]. This approach allows each UAV to make  
Figure 28: Illustration of decentralized control for UAV formation. Figure 27: Two-way flow of control for virtual construct methods. decisions based on information from neighboring UAVs and the surrounding environment, instead of relying on a leader UAV or a global control system. Figure 29 illustrates a decentralized behavior-based control approach for UAVs. The desired behaviors are listed in separate groups, including moving to the destination, avoiding obstacles, and maintaining the formation. These behaviors represent important goals that the UAV needs to accomplish during its operation. These behaviors are then fed into the behavioral coordinator, where they are coordinated and processed to determine the appropriate control strategy for the UAV. The coordinator plays an important role in balancing different behaviors. It ensures that the UAV can move toward a target, avoid obstacles, and maintain a stable formation. Finally, information from the behavioral coordinator is sent to the UAV controller, which determines specific control commands for the UAV, such as adjusting its speed, direction, or distance from other UAVs in the formation. The behavior-based decentralized method is a popular approach in UAV formation control. It helps UAVs maintain distance and formation shape without a single control center. Each UAV makes decisions based on information from itself and neighboring UAVs, instead of receiving commands directly from a leader UAV or a centralized control system [99]. One of the key techniques in this method is to use the weighting adjustment between behaviors, such as maintaining distance, avoiding collisions, and approaching the target, to calculate the UAV control input [100]. In addition, many studies have proposed combining this method with reinforcement learning to optimize UAV control in dynamic obstacle environments. For example, one study used a recurrent neural network to encode historical information about UAV positions and obstacles, which helps the UAV predict and adjust its trajectory more effectively [101]. Some other methods apply nonlinear consensus models to ensure that UAVs maintain formation shape even in the presence of environmental disturbances [102]. One of the important advantages of this method is the ability to maintain formation even when obstacles or changes in the environment appear. Instead of planning fixed actions in advance, each UAV can adapt to the actual situation through behaviors such as target tracking, collision avoidance, following moving walls, and adjusting position in the formation. In addition, some studies have proposed combining this method with decentralized collision avoidance algorithms. The UAV only uses the relative position information between the UAVs and obstacles to adjust the direction without requiring data from the lead UAV [100]. Furthermore, behavior-based control methods also play an important role in multi-agent systems and robot swarms. It allows robots to operate according to a simple set of rules but still achieve complex collective behavior, such as maintaining formation while moving or coordinating the execution of tasks in communication-limited  
Figure 29: Basic idea of a behavior-based UAV control model. environments [103]. Thanks to this ability, the method has been applied not only in UAVs but also in autonomous robot groups on land and underwater [104]. Compared with other formation control methods such as leader-follower or virtual structure, the decentralized behavior-based method has a great advantage in scalability and adaptability to changing environments. This makes it a suitable choice for many practical applications, from space surveillance, search and rescue, to UAV formation deployment in military missions [100].