<p>This study focuses on the implementation and enhancement of backstepping control for quadrotor trajectory management by integrating fuzzy logic. The aim is to address the limitations of conventional backstepping methods in balancing precision and energy consumption, offering a more effective and adaptive control strategy. Three control strategies were developed and compared: conventional backstepping with low-gain and high-gain settings, and a novel fuzzy backstepping approach. The fuzzy backstepping method dynamically adjusts control gains based on quadrotor position and error feedback, integrating fuzzy logic to achieve adaptive performance. Each method was evaluated for trajectory tracking accuracy and energy consumption. The proposed fuzzy backstepping method effectively eliminated the tracking errors associated with low-gain backstepping and achieved precision comparable to high-gain settings. Unlike high-gain backstepping, it maintained minimal energy consumption, providing an optimal balance between accuracy and efficiency. The fuzzy backstepping approach demonstrated superior performance, energy efficiency, and robustness compared to conventional methods. These results highlight its potential as a reliable control strategy for quadrotor applications in diverse fields such as surveillance, environmental monitoring, and aerial robotics.</p>