A comprehensive survey of UAV control algorithms: Integrating classical methods with artificial intelligence for enhanced trajectory tracking

The development of reliable control systems for unmanned aerial vehicles (UAVs) requires accurate modeling of dynamics and a control architecture capable of handling nonlinearity, external disturbances, and parameter uncertainty. This study addressed that challenge through a comprehensive survey of UAV control strategies, including classical linear controllers (such as PID, LQR, and MPC), advanced nonlinear methods [such as backstepping, sliding mode control (SMC), H-infinity, and active disturbance rejection control (ADRC)], and intelligent control algorithms like fuzzy logic and artificial neural networks (ANN). Based on this, the paper proposed a hybrid ANN-PID controller, where the neural network dynamically adjusts the PID coefficients to enhance adaptability and robustness. The method was evaluated through simulations on the UAV model using Euler–Lagrange dynamics. The quantitative results showed that ANN-PID achieves the lowest steady-state error (0.0229 m), nearly equivalent to PID (0.0230 m) but significantly superior to LQR (0.0807 m, an improvement of about 72%). Regarding rise time, ANN-PID responds quickly (~2.01 s), similar to PID and much faster than LQR (~7.4 s). Although the overshoot of all controllers is high, ANN-PID still achieves a lower value than PID and significantly reduces it compared to LQR under noisy conditions. These results affirm the superiority of the ANN-PID hybrid structure in UAV control, especially in nonlinear and complex dynamic environments.