Nonlinear disturbance observer-based adaptive flight control for nano quadcopter systems in strong wind gusts

Nano quadcopter unmanned aerial vehicles (nQUAVs) offer easy deployment, high manoeuvrability, scalability, and low cost, but their small size and lightweight structure make them highly susceptible to external disturbances and system uncertainties, while limited onboard computation constrains the complexity of control algorithms. This paper presents an Active Adaptive Bidirectional Fuzzy Brain Emotional Learning (AA-BFBEL) controller for precise trajectory tracking and disturbance rejection in uncertain environments. Unlike conventional passive methods, the AA-BFBEL integrates a computationally efficient disturbance observer based on an Extended State Observer (ESO) into the BFBEL framework to estimate wind forces and system uncertainties in real time, which are then actively compensated without prior training. This design is particularly suited to nQUAVs with short flight durations and limited processing resources, in contrast to reinforcement or deep learning-based controllers. The controller was validated through simulations and real-world experiments on the Crazyflie nano quadcopter, tracking complex three-dimensional figure-8 trajectories—a demanding test scenario for small UAVs. Across all cases, the AA-BFBEL significantly outperformed the baseline BFBEL, conventional PID, sliding mode control (SMC), and active disturbance rejection control (ADRC), achieving up to 74% higher tracking accuracy and nearly 61% faster settling time under strong wind conditions. These results demonstrate that the AA-BFBEL delivers high precision, robustness, and adaptability, providing an effective and reliable control solution for nQUAV operations in dynamic and challenging environments.