Disturbance-Resilient Formation Tracking of Tethered Space Net Robots via Distributed Lyapunov-Based MPC

Tethered space net robots (TSNRs) offer a flexible and promising configuration for active space debris removal. This study investigates the formation tracking control of TSNR for debris capture in the presence of unknown bounded disturbances. To enhance tracking accuracy, an observer-embedded distributed Lyapunov-based model predictive control (DLMPC) framework is proposed. By integrating real-time estimation errors directly into the predictive model, the framework effectively mitigates the mismatch between predicted and actual system dynamics. In addition, a worst-case contraction constraint is developed to ensure recursive feasibility and robust stability. Numerical experiments demonstrate that the proposed method significantly improves formation tracking precision and enhances resilience against disturbances compared to standard DMPC and auxiliary control strategies.