Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms

2026-05-04

AI Summary

This work addresses the complex dynamics, model uncertainties, and real-time control challenges encountered by hook-equipped quadrotors performing autonomous grasping and placement tasks between moving platforms. To this end, we propose an integrated framework combining digital twin technology with a robust adaptive model predictive control (MPC) strategy. A high-fidelity digital twin is developed using MuJoCo, and we innovatively incorporate zeroth-order robust optimization (zoRO) for uncertainty propagation alongside extended Kalman filtering (EKF) for online parameter estimation. This approach simultaneously enforces state and control constraints while balancing performance and computational efficiency. Both simulation and real-world flight experiments demonstrate that the proposed method achieves high-precision trajectory tracking and enables stable, robust, and efficient aerial transportation operations.

Abstract

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.

Problem

• aerial transportation
• moving platforms
• model uncertainty
• robust control
• hook-based manipulator

Innovation

• model predictive control
• digital twin
• robust adaptive control
• zero-order robust optimization
• aerial manipulation