MPC-based strategy for UAV slung-load systems: evaluation of energy consumption per unit projected distance

The integration of low-altitude economy and smart logistics will revolutionize remote and emergency deliveries, and UAV slung-load systems are critical components in this transformation. However, traditional control methods for these systems mainly focus on position tracking and anti-sway capabilities; they often neglect crucial aspects like energy quantification and the dynamic coupling between the UAV and its cargo. This study uses the Newton-Euler method to build a two-mass dynamic model, which includes aerodynamic drag and cable tension. It also innovatively proposes the "energy consumption per unit projected distance" index to quantify the correlation between energy consumption and system motion. Additionally, the study applies the semi-implicit Euler method for model discretization and rigid constraint correction, then uses model predictive control (MPC) with receding horizon prediction to achieve low-energy transportation under reference trajectories. Real mountain simulations show that the energy-saving MPC reduces energy consumption per unit projected distance by 24.51% compared with traditional PID. It also has better average cargo acceleration and compliant position deviation, which demonstrates its application value.