用数字孪生与自适应控制，实现移动平台间无人机钩取运输的精准稳定作业。
Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
- 基于MuJoCo构建无人机钩爪数字孪生模型，用于预测控制
- 融合zoRO与卡尔曼滤波，应对气动扰动和负载不确定性的挑战
- 在仿真与实飞中验证了高鲁棒性与实时性能，适合动态物流场景
本文提出一种新型模型预测控制（MPC）方法，用于配备钩爪的无人机在移动平台间实现自主抓取与放置。为准确快速建模复杂动力学，构建了基于MuJoCo的四旋翼钩爪数字孪生模型，并作为MPC的预测模型。针对预测模型中的不确定性（如气动效应和未知载荷），提出一种鲁棒自适应MPC方法。通过系统集成零阶鲁棒优化（zoRO）进行不确定性传播，以及扩展卡尔曼滤波（EKF）进行参数估计，确保约束满足、高性能与计算效率。该方法在复杂仿真场景及真实飞行实验中得到验证。
原文摘要 · Abstract (English)
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.
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