Disturbance Rejection Control Optimization for UAVs Based on Sliding Mode Disturbance Observer

To address the challenges of maintaining flight stability and control precision for unmanned aerial vehicles (UAVs) under complex disturbance environments, this paper proposes a disturbance rejection control optimization strategy based on a Sliding Mode Disturbance Observer (SMDO). The proposed method integrates the robustness of sliding mode control with the real-time compensation capability of a disturbance observer, and introduces a particle swarm optimization algorithm to adaptively tune key parameters. This enhances the system’s responsiveness and adaptability to external disturbances. The designed control framework is theoretically validated through Lyapunov stability analysis and implemented on a nonlinear six-degree-of-freedom UAV simulation platform. Experimental results demonstrate that the proposed approach significantly improves attitude tracking accuracy, disturbance recovery time, and control input smoothness compared to conventional control strategies, exhibiting excellent disturbance rejection performance and control consistency.