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Autonomous reinforcement learning control for space robot to capture non-cooperative targets

In recent years, space robot on-orbit service has become a research hotspot in many countries. Aiming at the task of capturing non-cooperative targets for space robot, a dual loop control method consisting of reinforcement learning control and PD control is proposed in this paper, which is used to control the attitude of space robot platform and the motion of manipulator arm. Firstly, a coupled dynamic model of the space robot including the motion of the base platform and the robot arm is established. Then, a dual loop control system is designed to control the movement of the robot arm and the attitude of the base platform. In the inner loop, the controller is designed by combining the reinforcement learning and fuzzy theory to control the motion of the end of the robot arm. In the outer loop, the attitude of the base platform is stabilized by PD control. Finally, the proposed control method is used for numerical simulation and compared with the traditional PD control method to verify the effectiveness of the proposed control method. The results show that the robot arm movement process under the control of reinforcement learning is stable and the control precision is high. Compared with the traditional PD control method, it has a certain self-learning ability and is more suitable for the non-cooperative characteristics of the catching targets.