Simulation results demonstrate improved stability and effective state estimation with a Kalman filter in UAV payload systems, suggesting advancements in outdoor transportation solutions.
Suspended-payload UAVs face a core dilemma in outdoor transportation: their stability is severely compromised by payload swing, yet conventional anti-swing controllers depend on real-time state feedback that is difficult to obtain without external sensors. To resolve this, we propose a fully integrated self-sensing anti-swing control strategy. Its key contribution lies in the seamless co-design of an external-sensor-free autonomous state estimation scheme and a coupled compensation control architecture: it eliminates the dependency on external sensors through an onboard estimation solution, actively suppresses swing via a coupled compensation framework, and rigorously guarantees stability through Lyapunov analysis. Specifically, an inertial measurement unit (IMU) at the suspension point fuses data with a Kalman filter for real-time payload state estimation. A sliding mode anti-swing controller is then integrated into the position control loop through dynamic coupling compensation. Strict Lyapunov-based analysis proves the bounded-error stability of this coupled system. High-fidelity simulations in CoppeliaSim under various scenarios validate the strategy’s engineering feasibility and synergistic performance. Results demonstrate that our method achieves high-precision state estimation and effective swing suppression, offering a practical and reliable solution for outdoor UAV transportation.
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Lin et al. (2025) studied this question.
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