## 1 Introduction
For the fast subsystem, the velocity difference feedback controller is used to actively suppress the elastic vibration of the joints' flexibility. For the slow subsystem, a buffer compliance control scheme based on reinforcement learning (RL) is proposed. The proposed reinforcement learning consists of two modules: associative search network (ASN) and adaptive critic network (ACN). ASN is used to approximate unknown nonlinear terms of mixed systems; the ACN adopts the online learning method. The learning strategy of RL obtains the original error evaluation signal through the performance evaluation unit, this error evaluation signal is coupled with ACN to generate the reinforcement signal. Then, the updated result is used as the learning rule of the neural network to train the neural network weight adaptive law of ASN and ACN, which can adjust and optimize the control strategy in real time. For the reinforcement learning strategy, [PERSON] et al. (2019) obtained the system dynamics model of space robots by reinforcement learning, by comparison with the traditional PD control method, that shows the self-learning ability of the reinforcement learning strategy. [PERSON] (2019) proposed deterministic artificial intelligence that can applied to both unmanned underwater vehicles and space robotics. [PERSON] and [PERSON] (2019) studied the control and stability issues of a trajectory tracking of an n-link rigid robot manipulator, and obtained an optimal control signal by a reinforcement learning strategy. [PERSON] et al. (2019) proposed a reinforcement learning strategy to investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle with external disturbances, control input nonlinearities and model uncertainties. On this basis, the proposed control scheme can absorb the impact energy generated in the collision process through the stretching and compression of the built-in spring in the collision capture phase. In the stable control phase, the control strategy based on reinforcement learning is used to actively turn on and off the joints' actuators to ensure that the joints' actuators will not be overloaded and damaged. In addition, the reinforcement learning strategy has the advantage of not needing the precise dynamics model of the hybrid system and can effectively improve the intelligence and reliability of the on-orbit acquisition operation of the space robot. The numerical simulation shows that the proposed control scheme can not only effectively absorb the impact energy generated by the on-orbit capture, but also open and close the joint actuators in a timely way when the impact energy is too large, which can avoid overload and damage to the joint actuators. The paper is organized as follows: in Section 2, the compliant mechanism and buffer compliance strategy are introduced. In Section 3, the dynamic model of the space robot capturing a non-cooperative target spacecraft is established. In the same section, the impact effect during the capturing operation phase is discussed. In Section 4, a reinforcement learning control algorithm combined with a compliant mechanism is proposed to achieve buffer compliance control and its stability is verified by introducing the suitable Lyapunov function. In Section 5, numerical simulations are carried out to validate the proposed buffer compliance control strategy. Finally, the conclusions are given in Section 6.