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

TL;DR

This paper introduces a robust adaptive model predictive control method for hook-based aerial transportation between moving platforms, utilizing a digital twin and advanced uncertainty handling to ensure high performance and safety.

Contribution

A novel robust adaptive MPC approach combining digital twin modeling, zoRO-based uncertainty propagation, and EKF for real-time parameter estimation in aerial manipulation tasks.

Findings

The proposed method achieves robust constraint satisfaction in complex simulations.

Real-world experiments validate the effectiveness of the control approach.

The digital twin enables accurate and rapid modeling of quadcopter dynamics.

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.