Data Driven Impedance Control of Space Robot Capturing a Target with Model Uncertainties

Abstract

The space manipulator system needs to be autonomous to perform on-orbit operations such as satellite servicing, refueling, and active debris removal, especially the removal of non-cooperative space targets (NCSTs). The impact phase is the most critical, as the end effector must maintain stable contact with the NCST to prevent separation without disturbing the free-floating base. Further, the harsh environment increases complexity during post-capture manipulation due to uncertainties in dynamics, ranging from internal factors such as joint friction and unknown inertial parameters to external disturbances, which challenge traditional model-based control methods. This paper proposes an integrated control that combines impedance control with an estimation method to address model uncertainty. The controller regulates the mechanical impedance of the end effector to manage physical interaction by forcing it as a mass-spring-damper system. This ensures the effective absorption of impact energy and damping of relative velocity without disturbing the base motion. To make the system robust to model uncertainties, a stochastic prediction model, such as a GP, is employed to estimate unmodeled parameters and environmental disturbances. The robustness of the control framework against model uncertainties is demonstrated through reductions in input impedance force and relative acceleration to maintain contact stability. Furthermore, SGP is employed to reduce the computation cost and speed up the estimation process.