Adaptive Neuro-Fuzzy Sliding Mode Tracking for Quadrotor UAVs

Quadrotor systems offer significant potential, yet precise trajectory tracking remains challenging due to their complex dynamics and nonlinear characteristics. In particular, the unmodeled dynamics of the translational subsystem and the nonlinearity of the rotational subsystem present substantial obstacles. This study proposes a dual-loop structure integrating neural networks (NN) and fuzzy control techniques. By combining the sliding-mode control (SMC) method with adaptive laws, the approach achieves a fast tracking response, improved transient performance, and reduced chattering. An adaptive neural control based on a switching surface addresses the unmodeled dynamics of the outer loop and performs position control. Meanwhile, a dissipative fuzzy sliding-mode controller for the inner loop manages nonlinearity and input uncertainty, ensuring robust attitude control. The designed controllers’ stability was verified using Lyapunov theory, and the simulation of various trajectories demonstrated the robustness and potential of the proposed dual-loop neuro-fuzzy tracking controller.