<p>This article proposes an anti-disturbance adaptive neural sliding mode flight tracking control scheme to address the challenges of disturbances and uncertainties in unmanned helicopters. A neural network observer is employed to manage uncertainties. Additionally, to tackle disturbances and neural network estimation inaccuracies, a nonlinear disturbance observer is introduced. To further enhance system robustness and address estimation errors from these observers, using the backstepping method, a sliding mode controller is designed. This sliding mode manifold outperforms traditional ones by better handling nonlinear systems, converging faster, and reducing chattering. The application of Lyapunov’s theory substantiates the system’s stability. Finally, simulation results are presented, demonstrating the potency of the proposed approach in ensuring overall system stability and resilience.</p>