## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Based Control Algorithm
#### 6.3.1 ANN-Based Control Approaches
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Proposed Method** & **Main Contribution** & **Advantages** & **Limit** \\ \hline \multirow{3}{*}{[176]} & Fully Tuned RBF Neural & Real-time system identification & Automatically selects the & \\  & Networks with & for quadcopter using RBF NN & optimal number of neurons; & Complex to implement; requires \\  & MRAN/EMRAN & with automatically adjusted & high prediction accuracy; short & real flight data for training \\  & & number of hidden neurons. & training time. & \\ \hline \multirow{3}{*}{[177]} & RBF Neural & Combining multi-task controller & Flexible in switching between & \\  & Network-Based & with general learning model & controllers; combines the & \\  & Generalized Learning & (GLM) based on Neural & advantages of traditional PID & \\  & Model & Network & and neural networks & \\ \hline \multirow{3}{*}{[182]} & Terminal Sliding Mode & \multirow{3}{*}{Tier 2 UAV swarm consensus} & Only need information from & \\  & combined with Chebyshev & & tracking controller & neighboring agents; good \\  & Neural Networks & & matching controller & phenomeon & \\ \hline \multirow{3}{*}{[183]} & Terminal Sliding Mode + & \multirow{3}{*}{Thebyshev Neural Networks} & Combining continuous & \\  & & quadratic sliding mode & Good anti-interference & \\  & Chebyshev Neural & controller (CSOSMC), & (completely eliminates & \\  & Networks & Fuzzy-Chebyshev network & vibration); stability is proven by & Complex controller design \\  & & (FCN), and adaptive control & Lyapunov method & \\  & method & & & \\ \hline \multirow{3}{*}{[184]} & Chebyshev & Approximating nonlinear & \multirow{3}{*}{Proposed method does have the } & \\  & Polynomials-Based (CPB) & functions using neural networks & approximator in some functional & Difficult to apply to complex \\  & Unified Model Neural & based on Chebyshev & approximation with & dynamic systems \\  & Networks & polynomials & considerable reduction in & \\  & & & learning time & \\ \hline \multirow{3}{*}{[187]} & Convolutional Neural & \multirow{3}{*}{Use CNN to predict future } & Avoid conflicts in the planning & \\  & Network (CNN) & position and update in real time & phase; adapt to dynamic & \\  & & & behavior & \\ \hline \multirow{3}{*}{[188]} & CNNs (Convolutional & \multirow{3}{*}{Using CNN for landslide } & T integrates multiple data & Efficiency depends on CNN \\  & Neural Networks & detection from UAV images & (image + gradient)high accuracy & design; requires high quality \\  & & & (90%). & input data \\ \hline \multirow{3}{*}{[189]} & BC (Temporal & \multirow{3}{*}{Using TCN instead of LSTM to predict motion for autonomous } & Improved calculation speed and & \\  & Convolutional Networks & predict motion for autonomous  vehicles & efficiency; prediction of both & \\  & & vehicles & vehicle and pedestrian & \\ \hline \multirow{3}{*}{[193]} & RNN (Recurrent Neural  Networks) & Consensus control of unmanned & Lyapunov stability; effective in & \\  & Networks & helicopter UAV; leader-follower & UAV swarm flight & \\  & method using RNN & & \\ \hline \multirow{3}{*}{[194]} & RNN-MPC (Recurrent  Neural Network-Based  Model Predictive Control) & Using RNN as prediction model & Distributed hierarchical control & \\  & Neural Network-Based & in MPC with adaptive update & system; online weight update to \\  & Model Predictive Control & rule to adjust weights & improve model accuracy. & \\ \hline \multirow{3}{*}{[195]} & Annealing Recurrent  Neural Network and & Using annealing recurrent & Reduce energy consumption and & \\  & Neural Network and & neural networks to search for & anti-chattering; achieves optimal & \\  & Extremum Seeking & extreme values, optimizing & configuration and minimum & \\  & Algorithm & UAVs flying in tight formation & energy requirements & \\ \hline \multirow{3}{*}{[196]} & \multirow{3}{*}{RNN and PSO (Particle  Swarm Optimization)} & Multi-agent decision model & \multirow{3}{*}{Adaptive learning, maximizing } & \\  & RNN and PSO, with input & using RNN and PSO, with input & \\ \cline{1-1}  & Swarm Optimization & being the previous strategies of & group benefits & \\ \hline \hline \end{tabular}
\end{table}