Fixed-Time Dynamic Landing of Quadrotors using Adaptive Unscented Kalman Filtering and Nonlinear Model Predictive Control
For quadrotor operators needing reliable landing on moving platforms, this work offers a practical solution with demonstrated hardware validation, though it is an incremental combination of existing techniques.
This paper presents a framework for dynamic landing of quadrotors on moving platforms, integrating nonlinear model predictive control with a minimum-jerk trajectory planner for fixed-time touchdown and an adaptive unscented Kalman filter for robustness. Hardware experiments show repeatable landings and improved velocity prediction over EKF/UKF methods.
This paper introduces an estimation and control framework for dynamic landing of multi-rotor uncrewed aerial vehicles on moving platforms. The proposed method integrates nonlinear model predictive control with a real-time minimum-jerk trajectory planner that enforces a prescribed touchdown time, enabling consistent timing during the terminal descent. To enhance robustness in the presence of time-varying sensing quality, we utilize an adaptive unscented kalman filter that updates the process and measurement noise statistics online. In addition, we provide a reference feasibility analysis showing that minimum-jerk references induce bounded thrust and torque commands under standard tracking hypotheses. The proposed framework is evaluated in simulation and hardware experiments, and it is shown to achieve repeatable landings and improved platform velocity prediction accuracy relative to EKF/UKF-based methods.