Liu Dongbo, Chen Li. Impedance control base on adaptive fuzzy composite nonlinear feedback for space robot on-orbit truss assembly operation. Mechanics in Engineering, xxxx, x(x): 1-11. doi: 10.6052/1000-0879-23-465
Citation: Liu Dongbo, Chen Li. Impedance control base on adaptive fuzzy composite nonlinear feedback for space robot on-orbit truss assembly operation. Mechanics in Engineering, xxxx, x(x): 1-11. doi: 10.6052/1000-0879-23-465

IMPEDANCE CONTROL BASE ON ADAPTIVE FUZZY COMPOSITE NONLINEAR FEEDBACK FOR SPACE ROBOT ON-ORBIT TRUSS ASSEMBLY OPERATION

  • Space truss is a kind of important infrastructure of space station, which plays an irreplaceable role in the construction of space station. Considering the space occupied by the assembled space truss and the carrying capacity of the rocket, it is generally adopted to launch the truss components into space and complete the assembly in orbit. In order to carry out space missions such as assembly and maintenance of space truss, the force posture impedance control of space robot in orbit insertion and extraction operation is studied. Using the kinematic constraints of the replaced plug, the kinematic relationship of the plug in the carrier coordinate system is established. Based on the principle of impedance control, a second-order linear impedance model is established according to the dynamic relationship between the plug posture and its output force, to realize the accurate control of the output force during the insertion and extraction operation. An adaptive fuzzy combined nonlinear feedback (CNF) control strategy is proposed. To reduce the influence of uncertain parameters and improve the control accuracy of posture, the uncertain parameters are fitted by the fuzzy controller as the compensation torque of the CNF controller. The convergence of the system is proved by Lyapunov principle, and the effectiveness of the controllable strategy is verified by simulation analysis.
  • loading

Catalog

    Turn off MathJax
    Article Contents

    /

      Return
      Return
        Baidu
        map