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Volume 7 Issue 5
Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(5): 1417-1424
Jing Cheng and Li Chen, "The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1417-1424, Sept. 2020. doi: 10.1109/JAS.2018.7511180
Citation: Jing Cheng and Li Chen, "The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1417-1424, Sept. 2020. doi: 10.1109/JAS.2018.7511180
shu

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

doi: 10.1109/JAS.2018.7511180
  • 1.

    School of Aerospace Engineering, Tsinghua University, Beijing 100084, China

  • 2.

    School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China

Funds:

the National Natural Science Foundation of China 11372073

the National Natural Science Foundation of China 11072061

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    Abstract
  • In this paper, the dynamic evolution for a dual-arm space robot capturing a spacecraft is studied, the impact effect and the coordinated stabilization control problem for post-impact closed chain system are discussed. At first, the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach, and the dynamic equations of a spacecraft are obtained by Newton-Euler method. Based on the results, with the process of integral and simplify, the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law. The closed chain system is formed in the post-impact phase. Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics. With the closed chain constraint equations, the composite system dynamic equations are derived. Secondly, the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter. In order to overcome the effects of uncertain system inertial parameters, the recurrent fuzzy neural network is used to approximate the unknown part, the control method with $\pmb H_{{\infty }}$ tracking characteristic. According to the Lyapunov theory, the global stability is demonstrated. Meanwhile, the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators. At last, numerical examples simulate the response of the collision, and the efficiency of the control scheme is verified by the simulation results.

     

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