Priority-Based Switching Model Predictive Control for Sequential Manipulation of Space Robot

In this paper, a priority-based switching model predictive control (SMPC) method is proposed for space robots to execute sequential operation tasks efficiently. For a predefined series of subtasks, we develop a state-dependent SMPC approach that simultaneously determines control inputs and switching times. By incorporating switching within the prediction horizon, the method improves overall task efficiency and smooth transitions. However, it may impact individual subtask performance due to potential conflicts between objectives. To address this, a soft-priority concept modulates the importance of each stage within the SMPC framework, offering a balanced and practical solution. Numerical simulations verify the method's effectiveness and analyze how preference-based priorities influence overall task performance.