Adaptive and Predictive Control of UAS in Train-Drone Delivery System

This paper addresses the control of a quadrotor operating in a hybrid train-drone delivery system, focusing on payload uncertainty and landing on a moving railway platform. The considered mission includes take-off from a train entering a logistics terminal, cruise flight to a delivery location, and dynamic re-landing on the departing train. A hierarchical control architecture based on Model Predictive Control (MPC) is presented. It integrates trajectory generation, constrained MPC trajectory tracking, and an online parameter adaptation scheme based on recursive least squares for mass estimation. The adaptive model is embedded in the MPC prediction model, allowing real-time compensation of payload variations without direct payload measurements. Dynamic landing is formulated as a time-varying constrained tracking problem where the landing target evolves according to the train kinematic model. Simulation results validate the proposed architecture across the mission phases, showing accurate trajectory tracking, robustness to payload mismatch, and reliable landing on a moving platform.