Li Chen

A space robot inevitably impacts a satellite in a capture operation. If its fragile joints are not protected, they may be damaged, resulting in failure of the capture operation. Thus, a spring damper buffer device (SDBD) is added between the joint motors and manipulators to absorb the impact energy, and provide a compliance strategy matched with the SDBD for stable control of the hybrid system. The dynamic modes of the dual-arm space robot open-loop system and target satellite system are established by the Lagrange function before capture. From the momentum theorem, velocity constraints, closed-chain geometric constraints, and Newton's third law, the closed-chain dynamic model of the hybrid system after capture is obtained, and the impact effect and impact force are calculated. For stable control of the hybrid system while limiting joint? impact torque within the safe range, an adaptive fractional-order super-twisting sliding mode compliance control strategy matching the SDBD is proposed. It can effectively improve the fast convergence and trajectory tracking performance of the system whose velocity and acceleration cannot be measured. The stability of the hybrid system is demonstrated by the Lyapunov theorem, and the anti-impact performance of the SDBD and the effectiveness of the compliance strategy are demonstrated through numerical simulation.

Aiming at the force position control problem of the on-orbit insertion and extraction operation of the free-floating space robot, the system dynamics model is established. According to the interaction between the end of manipulator and the environment, the second-order impedance model is established. In order to improves the calculation efficiency, the above models are reconstructed to avoid the use of acceleration signal by introducing filtering operation. This is also conducive to the application of robot actual control. Then, an estimator requiring only the system inertia matrix is designed to compensate the modeling uncertainty, external bounded disturbance and impact effect in the process of inserting and extracting. Its structure is simple and reliable. Only one control parameter needs to be adjusted, which greatly reduces the amount of calculation. Considering that the on-orbit operation of insertion and extraction is a kind of precision operation, its control system needs to have a high-quality control performance. By introducing the prescribed performance method, the tracking error is constrained within the given range and to ensure the transient performance and steady-state performance of the control system is ensured. Finally, three simulation conditions are designed, and the results are presented to verify that the proposed algorithm has a faster convergence speed compared with traditional sliding mode controller. It can achieve vertically inserting and accurate force tracking of the manipulator end.

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified.

In order to prevent joints from being damaged by impact force in a space robot capturing satellite, a spring-damper device (SDD) is added between the joint motor and manipulator. The device can not only absorb and attrition impact energy, but also limit impact force to a safe range through reasonable design compliance control strategy. Firstly, the dynamic mode of the space robot and target satellite systems before capture are established by using a Lagrange function based on dissipation theory and Newton-Euler function, respectively. After that, the impact effect is analyzed and the hybrid system dynamic equation is obtained by combining Newton’s third law, momentum conservation, and a kinematic geometric relationship. To realize the buffer compliance stability control of the hybrid system, a reinforcement learning (RL) control strategy based on a fuzzy wavelet network is proposed. The controller consists of a performance measurement unit (PMU), an associative search network (ASN), and an adaptive critic network (ACN). Finally, the stability of system is proved by Lyapunov theorem, and both the impact resistance of SDD and the effectiveness of buffer compliance control strategy are verified by numerical simulation.

The dynamic modeling, motion control and flexible vibration active suppression of space robot under the influence of flexible base, flexible link and flexible joint are explored, and motion and vibration integrated fixed-time sliding mode control of fully flexible system is designed. The flexibility of the base and joints are equivalent to the vibration effect of linear springs and torsion springs. The flexible links are regarded as Euler–Bernoulli simply supported beams, which are analyzed by the hypothetical mode method, and the dynamic model of the fully flexible space robot is established by using the Lagrange equation. Then, the singular perturbation theory is used to decompose the model into slow subsystem including rigid motion and the link flexible vibrations, and fast subsystems including the base and the joint flexible vibrations. A fixed time sliding mode control based on hybrid trajectory is designed for the slow subsystem to ensure that the base and joints track the desired trajectory in a limited time while achieving vibration suppression on the flexible links. For the fast subsystem, linear quadratic optimal control is used to suppress the flexible vibration of the base and joints. The simulation results show that the controller proposed in the paper can make the system state converge within a fixed time, is robust to model uncertainty and external interference, and can effectively suppress the flexible vibration of the base, links, and joints.

During the process of satellite capture by a flexible base–link–joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mode stabilization control is proposed to stabilize the system. First, the dynamic models of the fully flexible space robot and the captured satellite are established, respectively, and the impact effect is calculated according to the motion and force transfer relationships. Based on this, a dynamic model of the system after capturing is established. Subsequently, the system is decomposed into slow and fast subsystems using the singular perturbation theory. To ensure that the base attitude and the joints of the slow subsystem reach the desired trajectories, link vibrations are suppressed simultaneously, and a repetitive learning sliding mode controller based on the concept of the virtual force is designed. Moreover, a multilinear optimal controller is proposed for the fast subsystem to suppress the vibration of the base and joints. Two sub-controllers constitute the repetitive learning sliding mode stabilization control for the system. This ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints. Finally, the simulation results verify the effectiveness of the designed control strategy.