## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Based Control Algorithm
#### 6.3.1 ANN-Based Control Approaches
This method ensures stability in finite time by the Lyapunov approach and maintains the reference trajectory when only some UAVs in the system receive the reference signal [182]. ChNN is also used to design an attitude controller for a quadrotor, where it is combined with the continuous quadratic sliding mode control method and the Chebyshev fuzzy neural network. This method increases the resilience to disturbances and parameter uncertainties and completely eliminates the jitter phenomenon by Lyapunov stability [183]. In addition, ChNN is used in the CPB fusion neural network model to optimize learning speed. It significantly reduces training time compared to traditional neural networks while still maintaining the universal approximation capability [184]. Convolutional neural network (CNN) is one of the most important models in the field of deep learning, especially in image processing and natural language processing. Due to the remarkable achievements in many fields, CNNs have attracted great attention from both academia and industry. CNNs are designed to automatically learn and extract spatial features from input data through convolutional layers, sampling layers, and fully connected layers. Thanks to this special structure, CNNs can detect important features in images. This helps to improve the accuracy in recognition, classification, and prediction tasks. CNN models are not only limited to two-dimensional convolutions but also extended to one-dimensional and multi-dimensional convolutions. Advanced variants of CNNs such as ResNet, DenseNet, and EfficientNet have improved performance by optimizing the feature learning process and mitigating the gradient decay problem [185; 186]. CNN is also used to support the multi-agent Pickup and Delivery problem. A group of UAVs acting as guests must move in a shared environment without communicating with the host UAV group. Instead of using a static behavioral model based on past location data, this method applies CNN to predict the future location of the host UAV group, helping the guest UAV group plan an optimal path [187]. CNN is integrated into a UAV system to detect landslides by analyzing optical data collected from UAVs. The CNN model is trained to identify landslide-prone areas with high accuracy, reaching 90% in precision evaluation and 85% in F-score [188]. CNN is also applied to improve the prediction of vehicle movements in an autonomous navigation system. The model is improved by replacing LSTM with a Time Convolutional Neural Network. It speeds up the computation and improves the accuracy of motion prediction. The experimental results on the Agoverse 2 dataset show that the model has superior performance in motion trajectory prediction and computational efficiency compared to previous methods [189]. Recurrent neural networks (RNN) are suitable for sequential data processing problems such as speech recognition, online handwriting, machine translation, and time series forecasting. The highlight of RNN is the ability to remember information from previous steps through recurrent connections. It allows the model to learn and analyze the relationships between elements in the data series. Other variants of RNN have also been developed to enhance learning capabilities and apply to more complex problems such as image, video, and medical data processing. Multidimensional recurrent neural networks have extended the application scope of RNN to areas such as image segmentation and spatiotemporal data processing without facing the scaling problem of previous models [190; 191; 192]. An intelligent consensus-based formation control method for small, unmanned helicopters (SSUH) is developed with the help of RNN. The mathematical model of SSUH is built on a directed graph, in which RNN is used to learn the system uncertainties online and track the consensus among UAVs. The Lyapunov theory is applied to ensure the stability of the system [193]. RNN is integrated into the model predictive control (MPC) structure to control the formation flight of multiple quadrotor UAVs.