A robust model predictive control unified framework for autonomous rendezvous and docking with a tumbling target

Abstract

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation.