Resumen
The space target capturing task using the spacecraft-manipulator system (SMS) has special significance in on-orbit servicing due to its theoretical challenges and practical value. The contact force between the end effector (gripper) and the target exerted by the tumbling motion of the space target destabilizes the spacecraft base. A full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure was designed to buffer the transient impact force on the end joint. The introduction of a damping mechanism gives the space robot a variable stiffness and damping system, and a stiffness estimation algorithm is proposed to calibrate the system stiffness, as stiffness cannot be measured directly. The full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure is equivalent to a four-DOF tandem joint, and the whole-body dynamic model of the SMS endowed with a full-dimensional controllable damping mechanism (FDCDM) was established using the Kane equation. Then, an unknown input observer (UIO)-based identification theory is proposed to precisely estimate the internal flexibility torque and the corresponding joint stiffness. A model-based neural learning algorithm is proposed to update the variable parameter matrix of the observer. The simulation experiment results demonstrate that the flexibility torque and joint stiffness could be accurately estimated within the expected error, illustrating the feasibility and effectiveness of the proposed method.