## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Based Control Algorithm
#### 6.3.1 ANN-Based Control Approaches  
The ANNs are computational models inspired by biological neural systems. They consist of nodes connected to each other through adjustable weights [167]. ANNs work by receiving input data, processing it through hidden layers, and generating outputs based on the learning process from training data [168; 169]. Thanks to their abilities to learn and to adapt to the environment, ANNs are widely used in UAV swarm control to optimize coordination, automation, and intelligent decision-making [170; 171]. In UAV swarm control, ANNs can help UAVs learn to maintain formation, avoid collisions, and adapt to changing environments [116]. These models have opened many new directions  
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for the development of UAVs. They help to enhance the intelligence and autonomy of UAV swarms [172]. Some popular ANN models that have been studied and applied in swarm control are addressed as follows. Radial basis function neural network (RBFNN) has the ability to approximate universal functions. This helps to solve many problems in data classification, pattern recognition, and nonlinear system control. RBFNN consists of two main layers, including a hidden layer that uses kernel functions to determine the activation region in the input space, and an output layer that calculates the result based on the link weights. The construction and optimization of RBFNN are often based on fast clustering algorithms, statistical methods, or deep learning algorithms to optimize the number of neurons and network weights [173]. One of the important applications of RBFNN is handwritten character recognition, in which fast clustering methods help to build an effective classifier with high accuracy. In addition, RBFNN is also used in control systems and state estimation for complex nonlinear systems, through approximation models and optimal sub-filters to reduce errors [174]. Due to its fast computation, good generalization ability, and high accuracy, RBFNN is a useful tool in machine learning and artificial intelligence, contributing to improving performance in many fields of science and technology [175]. In UAV control, RBFNN supports the estimation and compensation of external disturbances, improving the stability of the system. In the combination of the leading-following model and control methods such as backstepping or sliding mode, UAVs can maintain formation and track trajectories more accurately. In addition, RBFNN is also applied in real-time UAV modeling. When integrated with the EMRAN training algorithm, the network can self-adjust the number of neurons without manual tuning. Compared to the MRAN method, EMRAN helps to significantly reduce training time while maintaining high accuracy [176]. In addition, RBFNN is also used in multi-mode adaptive controllers, combining time-varying linear models and nonlinear models learned from RBFNN. This method allows flexible switching between different control modes such as traditional PID or pole and zero tuning PID, helping the UAV system adapt quickly to changing environments [177]. Chebyshev Neural Network (ChNN) is an ANN model that uses Chebyshev polynomials to expand the input, helping to eliminate hidden layers and improve computational efficiency [178; 179; 180; 181]. ChNN plays an important role in UAV swarm control and multi-agent systems due to its ability to strongly approximate nonlinear functions and to improve controller stability. The use of ChNN helps to handle initial value problems with singularities. It is also a major challenge in numerical computation. ChNN is also applied in tracking consensus control for a quadratic multi-agent system. The UAVs in this system are modeled as connected undirected graphs, and the controller is designed based on the quadratic sliding mode. ChNN acts as a nonlinear function approximator to learn the unknown dynamics of the UAVs online, while a robust control algorithm is used to compensate for external disturbances and approximation errors.