ELM Neural Network Control of Attitude Management and Auxiliary Docking Maneuver after Dual-arm Space Robot Capturing Spacecraft

The coordinated control problems of attitude management and auxiliary docking operation for a dual-arm space robot capturing a spacecraft are discussed. Firstly, the dynamic equations of the closed-chain composite system after the capturing operation are established by the theorem of impulse, and the geometrical and kinematic conditions of the closed-loop constraints, and the impact effect on the composite system is analyzed. Secondly, an adaptive neural network control scheme based on the extreme learning machine (ELM) is designed for the closed-chain composite system to implement the motion stabilization under attitude disturbance and the auxiliary docking operation of the system after capturing. The ELM is used to approximate unknown dynamical model of the system because it can implement fast learning and only needs to adjust the output weight values of the network. In the proposed control method, the dynamic equations of the system needn't be linearly dependent on inertial parameters, and the precise system dynamic model isn't required. The weight adaptive law of ELM network and the robust items are designed through Lyapunov method, to guarantee the motion stabilization of the base under attitude disturbance and the precise position and attitude control during docking operation. The system stability is proved. The torques are distributed by the weighted minimum-norm theory to ensure the cooperation of the manipulators. Finally, the collision impact effect and the movement process of the closed-chain system are demonstrated through numerical simulation. The proposed control scheme can efficiently complete the motion control of both the base and the load, as well as the auxiliary docking operation.