Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
It addresses the challenge of autonomous aerial manipulation in dynamic environments, offering a practical solution for tasks like package delivery between moving vehicles.
This paper proposes a robust adaptive MPC for hook-based aerial pick-and-place between moving platforms, achieving robust constraint satisfaction and high performance in both simulation and real-world experiments.
This paper presents a novel model predictive control (MPC) approach for autonomous pick-and-place between moving platforms with a hook-equipped aerial manipulator. First, for accurate and rapid modeling of the complex dynamics, a digital twin model of the quadcopter equipped with a hook-based gripper, implemented in MuJoCo, is constructed and used as the predictive model for the MPC. To handle uncertainties of the predictive model (e.g. due to aerodynamics and uncertain payloads), a robust adaptive MPC approach is proposed. By systematic integration of zero-order robust optimization (zoRO) based uncertainty propagation and an extended Kalman filter (EKF) for parameter estimation, the MPC algorithm ensures robust constraint satisfaction, high performance, and computational efficiency. The effectiveness of the proposed method is evaluated in complex simulated scenarios and in real-world flight experiments.