Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Hybrid position/force control of uncertain robotic systems using neural networks

신경회로망을 이용한 불확실한 로봇 시스템의 하이브리드 위치/힘 제어

  • Kim, Seong-U ;
  • Lee, Ju-Jang (Dept. of Electrical Electronic Engineering, Korea Advanced Institute of Science and Technology)
  • 김성우 (현대전자산업(주) 생산기술연구소 생산시스템실) ;
  • 이주장 (한국과학기술원 전기 및 전자공학과)
  • Published : 1997.06.01
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Abstract

This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.

Keywords

References

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