Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase

Dual-arm space robots provide a promising solution for efficiently and flexibly completing complex on-orbit servicing tasks. However, their effectiveness is often hindered by strong dynamic coupling between the robotic arms and the satellite base in the microgravity environment. In this paper, both robotic arms are used as task arms, and a system model that considers the closed-loop constraint formed after a dual-arm space robot captures a target is established. On this basis, two types of control strategies, namely self-tuning control strategy (STC) and radial basis function neural network control strategy (RBF) are proposed for trajectory tracking of the target transferred or detumbled by the robot arms in the presence of system uncertainty or external disturbances. The control methods are verified and compared against the traditional computed torque control strategy (CTC) in terms of control accuracy and system robustness through simulation. The results demonstrate the excellent robustness of both RBF and STC strategies. RBF outperforms the other strategies in terms of control accuracy, while STC proves to be the most energy-efficient.