Neuro-Fuzzy Hybrid Position/Force Control for a Space Robot with Flexible Dual-Arms

  • Fuchun Sun
  • Hao Zhang
  • Hao Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)


A neuro-fuzzy (NF) hybrid position/force control with vibration suppression is developed in this paper for a space robot with flexible dual-arms handling a rigid object. An impedance force control algorithm is derived using force decomposition, and then singular perturbation method is used to construct the composite control, where an adaptive NF inference system is employed to approximate the inverse dynamics of the space robot. Finally, an example is employed to illustrate the validity of the proposed control scheme.


Fuzzy Inference System ANFIS Model Flexible Manipulator Rigid Object Space Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chiou, B.C., Shahinpoor, M.: Dynamic Stability Analysis of a Two-Link Force-Controlled Flexible Manipulator. ASME Journal of Dynamics Systems, Measurement and Control 112, 661–666 (1990)CrossRefGoogle Scholar
  2. 2.
    Matsuno, F., Yamamoto, K.: Dynamic Hybrid Position/Force Control of a Two Degree-of- Freedom Flexible Manipulator. Journal of Robotic Systems 11, 355–366 (1994)zbMATHCrossRefGoogle Scholar
  3. 3.
    Siciliano, B., Villani, L.: Two-time Scale Force and Position Control of Flexible Manipulators. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2729–2734 (2001)Google Scholar
  4. 4.
    Sudipto, S.: Robot Manipulation with Flexible Link Fingers, PhD thesis, California Institute of Technology, Pasadena, California (1996)Google Scholar
  5. 5.
    Yamano, M., Kim, J.S., Uchiyama, M.: Hybrid Position/Force Control of Two Cooperative Flexible Manipulators Working in 3D Space. In: Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, pp. 1110–1115 (1998)Google Scholar
  6. 6.
    Jang, J.–S.R.: ANFIS: Adaptive Network based Fuzzy Inference System. IEEE Transaction on Systems, Man and Cybernetics 3, 665–685 (1993)CrossRefGoogle Scholar
  7. 7.
    Bonitz, R., Hsia, T.: Internal Force based Impedance Control for Cooperating Manipulators. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 944–949 (1993)Google Scholar
  8. 8.
    Bruno, S., Villani, L.: Robot Force Control. Kluwer Academic, Boston (1999)zbMATHGoogle Scholar
  9. 9.
    Panella, M., Rizzi, A., Mascioli, F.M.F., Martinelli, G.: ANFIS Synthesis by Hyperplane Clustering. In: Proceedings of IFSA World Congress and 20th NAFIPS International Conference, vol. 1, pp. 340–345 (2001)Google Scholar
  10. 10.
    Chen, X., Jin, D., Li, Z.: Recursive Training for Multi-Resolution Fuzzy Min-Max Neural Network Classfier. In: Proceedings of International Conference on Solid-State and Integrated-Circuit Technology, vol. 1, pp. 131–134 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Fuchun Sun
    • 1
  • Hao Zhang
    • 1
  • Hao Wu
    • 1
  1. 1.Department of Computer Science and Technology, State Key Lab of Intelligent Technology and SystemsTsinghua UniversityBeijingP.R. China

Personalised recommendations