PID Control and Alternative Control Approaches in UAV Systems

In the control of unmanned aerial vehicles (UAVs), proportional–integral–derivative (PID) controllers continue to play a central role due to their simplicity, rapid implementation, and low computational demand. However, the classical PID approach faces inherent limitations, including sensitivity to disturbances, complex tuning, and limited adaptability, which increasingly highlight the relevance of advanced control methods. Recent research has investigated adaptive, fuzzy, and fractional-order PID (FOPID) solutions, as well as methods based on artificial intelligence and predictive control techniques such as linear quadratic regulator (LQR), model predictive control (MPC), sliding mode control (SMC), and H∞ approaches, many of which provide improved robustness and performance. The purpose of this study is to review the application of PID control in UAV systems, to identify its limitations, and to present alternative control strategies that may become dominant in the future through the development of hybrid and intelligent systems.