Control of an Industrial PA10-7CE Redundant Robot Using a Decentralized Neural Approach

  • Ramon Garcia-Hernandez
  • Edgar N. Sanchez
  • Miguel A. Llama
  • Jose A. Ruz-Hernandez
Part of the Studies in Computational Intelligence book series (SCI, volume 465)

Abstract

This paper presents a discrete-time decentralized control strategy for trajectory tracking of a seven degrees of freedom (DOF) redundant robot. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The neural network learning is performed online using Kalman filtering. The motion of each joint is controlled independently using only local angular position and velocity measurements. The proposed controller is validated via simulations.

Keywords

Decentralized control High-order neural networks Extended Kalman filter Backstepping Industrial robot 

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References

  1. 1.
    Sanchez, E.N., Ricalde, L.J.: Trajectory tracking via adaptive recurrent neural control with input saturation. In: Proc. of International Joint Conference on Neural Networks, Portland, Oregon, pp. 359–364 (2003)Google Scholar
  2. 2.
    Santibañez, V., Kelly, R., Llama, M.A.: A novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators. IEEE Transactions on Fuzzy Systems 13(3), 362–372 (2005)CrossRefGoogle Scholar
  3. 3.
    Huang, S., Tan, K.K., Lee, T.H.: Decentralized control design for large-scale systems with strong interconnections using neural networks. IEEE Transactions on Automatic Control 48(5), 805–810 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Liu, M.: Decentralized control of robot manipulators: nonlinear and adaptive approaches. IEEE Transactions on Automatic Control 44(2), 357–363 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Ni, M.L., Er, M.J.: Decentralized control of robot manipulators with coupling and uncertainties. In: Proc. of the American Control Conference, Chicago, Illinois, pp. 3326–3330 (2000)Google Scholar
  6. 6.
    Karakasoglu, A., Sudharsanan, S.I., Sundareshan, M.K.: Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators. IEEE Transactions on Neural Networks 4(6), 919–930 (1993)CrossRefGoogle Scholar
  7. 7.
    Safaric, R., Rodic, J.: Decentralized neural-network sliding-mode robot controller. In: Proc. of 26th Annual Conference on the IEEE Industrial Electronics Society, Nagoya, Aichi, Japan, pp. 906–911 (2000)Google Scholar
  8. 8.
    Krstic, M., Kanellakopoulos, I., Kokotovic, P.: Nonlinear and Adaptive Control Design. John Wiley & Sons Inc., New York (1995)Google Scholar
  9. 9.
    Ge, S.S., Zhang, J., Lee, T.H.: Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time. IEEE Transactions on Systems, Man, and Cybernetics Part B 34(4), 1630–1645 (2004)CrossRefGoogle Scholar
  10. 10.
    Alanis, A.Y., Sanchez, E.N., Loukianov, A.G.: Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks. IEEE Transactions on Neural Networks 18(4), 1185–1195 (2007)CrossRefGoogle Scholar
  11. 11.
    Rovithakis, G.A., Christodoulou, M.A.: Adaptive Control with Recurrent High-Order Neural Networks. Springer, London (2000)CrossRefGoogle Scholar
  12. 12.
    Song, Y., Grizzle, J.W.: The extended Kalman filter as local asymptotic observer for discrete-time nonlinear systems. Journal of Mathematical Systems, Estimation and Control 5(1), 59–78 (1995)MathSciNetMATHGoogle Scholar
  13. 13.
    Higuchi, M., Kawamura, T., Kaikogi, T., Murata, T., Kawaguchi, M.: Mitsubishi clean room robot. Mitsubishi Heavy Industries, Ltd., Tecnical Review (2003)Google Scholar
  14. 14.
    Jamisola, R.S., Maciejewski, A.A., Roberts, R.G.: Failure-tolerant path planning for the PA-10 robot operating amongst obstacles. In: Proceedings of IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, pp. 4995–5000 (2004)Google Scholar
  15. 15.
    Kennedy, C.W., Desai, J.P.: Force feedback using vision. In: Proceedings of IEEE International Conference on Advanced Robotics, Coímbra, Portugal (2003)Google Scholar
  16. 16.
    Ramirez, C.: Dynamic modeling and torque-mode control of the Mitsubishi PA10-7CE robot. Master Dissertation, Instituto Tecnológico de la Laguna, Torreón, Coahuila, Mexico (2008) (in Spanish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ramon Garcia-Hernandez
    • 1
  • Edgar N. Sanchez
    • 2
  • Miguel A. Llama
    • 3
  • Jose A. Ruz-Hernandez
    • 1
  1. 1.Facultad de IngenieriaUniversidad Autonoma del CarmenCd. del CarmenMexico
  2. 2.Department of Electrical EngineeringCINVESTAV Unidad GuadalajaraCol. El Bajío, ZapopanMexico
  3. 3.Division de Estudios de Posgrado e InvestigacionInstituto Tecnologico de la LagunaTorreonMexico

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