Double-layer prescribed performance control of free-floating space manipulators after capturing unknown targets

This paper studies the parameter identification and prescribed performance control of the freefloating space manipulator after capturing unknown targets. A novel finite-time concurrent learning (FT-CL) parameter identification algorithm utilizing suitable historical data is proposed. This algorithm guarantees the finite-time convergence of the parameter estimation error without using the persistent excitation condition. Then, we apply the dual modeling approach and view the end-effector states as generalized coordinates of novel virtual dual dynamics. This technique bypasses solving the inverse kinematics and calculating the Jacobi matrix, laying the foundation for Jacobi-free pose control of the end-effector. Besides, stabilizers of prescribed performance reference acceleration are carefully designed in a double-layer form for regulating end-effector pose and base attitude. Finally, the Jacobi-free controllers satisfying the double-layer performance constraints are developed. The efficacy of the proposed parameter identification scheme and control strategy is validated through numerical simulations.