BACKSTEPPING ADAPTIVE CONTROL FOR GRASPED OBJECT OF SPACE MANIPULATORS WITH CLOSED-CHAIN BASED ON NEURAL NETWORK

Hybrid position and force control problem of grasped object of a free-floating space manipulator with closed kinematic chain was discussed. By combining the relationship of the linear and angular momentum conversation, closed kinematic chain restraints and multi-body dynamic method, the synthetical dynamic model of the free floating space manipulator grasping system was established. With uncertain parameters and external disturbance, an adaptive backstepping neural network control scheme was developed to track the desired trajectory of object, and the correspondent scheme of internal forces control was proposed synchronously. Therefore, the object position and internal force can be regulated simultaneously. The proposed control scheme needs not to linearly parameterize the dynamic equation of the system. Meanwhile, it can guarantee asymptotically stability of the system. A planar free-floating space manipulator was simulated to verify that the proposed control scheme can eliminate the effect of uncertain parameters on the system.