Integrated Hierarchical Control for Quadrotor-Slung Payload System

This study proposes a hierarchical control strategy for a quadrotor UAV carrying a cable-suspended payload, aimed at enhancing tracking accuracy and robustness against disturbances. The framework integrates a model predictive controller (MPC) for generating thrust and angular velocity commands, a radial basis function (RBF) neural network for real-time disturbance estimation and compensation, and an adaptive incremental nonlinear dynamic inversion (AINDI) controller for precise inner-loop angular velocity regulation. The dynamic model accounts for coupling effects between the UAV and payload. Lyapunov-based analysis ensures system stability, and extensive simulations validate the controller's effectiveness in disturbance rejection and trajectory tracking, demonstrating its applicability to real-world aerial transport tasks.