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Adaptive neural network second-order sliding mode control of dual arm robots

  • Le Anh Tuan
  • Young Hoon Joo
  • Le Quoc Tien
  • Pham Xuan Duong
Intelligent Control and Applications

Abstract

An adaptive robust control system is considered for dual-arm manipulators (DAM) using the combination of second-order sliding mode control (SOSMC) and neural networks. The SOSMC deals with the system robustness when faced with external disturbances and parametric uncertainties. Meanwhile, the radial basis function network (RBFN) is to constitute an adaptation mechanism for approximating the unknown dynamic model of DAM. The stability of model estimator-integrated controller is analyzed using Lyapuov theory. To show the effectiveness of proposed controller, a four DOFs-DAM is applied as an illustrating example. The results reveal that the controller works well, excellently adapt to no information of robot modeling.

Keywords

Dual-arm manipulator modelling estimation neural network robust adaptive control 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Le Anh Tuan
    • 1
  • Young Hoon Joo
    • 2
  • Le Quoc Tien
    • 3
  • Pham Xuan Duong
    • 3
  1. 1.Department of Automotive EngineeringVietnam Maritime UniversityHai PhongVietnam
  2. 2.Department of Control and Robotics EngineeringKunsan National UniversityKunsan, ChonbukKorea
  3. 3.Vietnam Maritime UniversityHai PhongVietnam

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