Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network

Two-Time Scale Tracking Control of Flexible Robots With Primal-Dual Inverse Reinforcement Learning

Flexible robots (FRs) are generally designed to be lightweight to achieve rapid motion. However, accompanying vibrations and modeling errors influence tracking control, especially in situations involving reference signal loss. This article develops a two-time scale primal-dual inverse reinforcement learning (PD-IRL) framework for FRs to perform tracking tasks with incomplete reference signals. First, consider the admissible policy as a nonconvex input constraint to guarantee the stable operation of the equipment. Then, FRs imitate the demonstration behaviors of an expert, including both rigid and flexible motions, to achieve a balance in tracking speed and vibration suppression. During the imitation process, nonconvex optimization problems of FRs are transformed into corresponding dual problems to obtain the global optimal policy. Moreover, employing multiple linearly independent paths to explore the state space simultaneously can improve convergence speed. Convergence and stability are studied rigorously. Finally, simulations and comparisons show the effectiveness and superiority of the proposed method.

Flexible payload transportation using cooperative space manipulators with statics compensation

This study focuses on the dynamics and cooperative control for two space manipulators transporting the flexible payload. The assumed mode method is used to discretize the flexible component. Based on the Lagrange's equations of second kind and Lagrange multiplier method, the dynamics model of system is built. To compensate for the disturbances from the payload acting on the manipulators, the boundary forces and torques of the payload are estimated based on the statics analysis. A radial basis function neural network (RBF NN) is adopted to approximate some unknown terms. A NN-based cooperative controller with statics compensation is proposed for such a space manipulation system to drive the manipulators and beam to the desired states. The stability of the controller is proven through Lyapunov theory. Numerical simulations via the constant-step generalized-α integrator and some experiments based on QArm platforms are performed to show the efficiency of the designed controller.

A comprehensive survey of space robotic manipulators for on-orbit servicing

On-Orbit Servicing (OOS) robots are transforming space exploration by enabling vital maintenance and repair of spacecraft directly in space. However, achieving precise and safe manipulation in microgravity necessitates overcoming significant challenges. This survey delves into four crucial areas essential for successful OOS manipulation: object state estimation, motion planning, and feedback control. Techniques from traditional vision to advanced X-ray and neural network methods are explored for object state estimation. Strategies for fuel-optimized trajectories, docking maneuvers, and collision avoidance are examined in motion planning. The survey also explores control methods for various scenarios, including cooperative manipulation and handling uncertainties, in feedback control. Additionally, this survey examines how Machine learning techniques can further propel OOS robots towards more complex and delicate tasks in space.

Reinforcement learning based variable damping control of wearable robotic limbs for maintaining astronaut pose during extravehicular activity

As astronauts perform on-orbit servicing of extravehicular activity (EVA) without the help of the space station's robotic arms, it will be rather difficult and labor-consuming to maintain the appropriate position in case of impact. In order to solve this problem, we propose the development of a wearable robotic limb system for astronaut assistance and a variable damping control method for maintaining the astronaut's position. The requirements of the astronaut's impact-resisting ability during EVA were analyzed, including the capabilities of deviation resistance, fast return, oscillation resistance, and accurate return. To meet these needs, the system of the astronaut with robotic limbs was modeled and simplified. In combination with this simplified model and a reinforcement learning algorithm, a variable damping controller for the end of the robotic limb was obtained, which can regulate the dynamic performance of the robot end to resist oscillation after impact. A weightless simulation environment for the astronaut with robotic limbs was constructed. The simulation results demonstrate that the proposed method can meet the recommended requirements for maintaining an astronaut's position during EVA. No matter how the damping coefficient was set, the fixed damping control method failed to meet all four requirements at the same time. In comparison to the fixed damping control method, the variable damping controller proposed in this paper fully satisfied all the impact-resisting requirements by itself. It could prevent excessive deviation from the original position and was able to achieve a fast return to the starting point. The maximum deviation displacement was reduced by 39.3% and the recovery time was cut by 17.7%. Besides, it also had the ability to prevent reciprocating oscillation and return to the original position accurately.