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KSME International Journal

, 18:1916 | Cite as

A robust control with a neural network structure for uncertain robot manipulator

  • In-Chul Ha
  • Myoung-Chul Han
Article

Abstract

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.

Key Words

Robot Manipulator Robust Control NN (Neural Network) Lyapunov Stability Bound Function 

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Copyright information

© The Korean Society of Mechanical Engineers (KSME) 2004

Authors and Affiliations

  1. 1.Center for Robot Technology & Manufacturing of Institute for Advanced EngineeringKyonggi- doKorea
  2. 2.Department of Mechanical EngineeringPusan National UniversityPusanKorea

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