Enhancing Quadrotor UAV Trajectory Tracking Using Adaptive Fuzzy PID Control

Unmanned aerial vehicles (UAVs) require precise and robust control strategies to ensure safe and efficient flight. This work focuses on the Adaptive Fuzzy PID (AFPID) controller as the main method, which integrates classical Proportional-Integral-Derivative (PID) control with fuzzy logic principles to achieve real-time parameter adaptation. The PID and fuzzy PID controllers are considered as baseline methods for performance comparison with the adaptive fuzzy PID. Simulation was done using MATLAB to evaluate the controllers in terms of trajectory tracking, settling time, Root Mean Square error and robustness under disturbance. Since PID and FPID exhibited similar response shapes to AFPID, only their performance metrics were reported for comparison. The results demonstrate that all controllers successfully stabilize the UAV without steady-state error, while AFPID provides slightly improved settling times and disturbance rejection compared to PID and FPID. These findings confirm the effectiveness of adaptive fuzzy control as a reliable solution for UAV path-tracking tasks.