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

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Neural network control by learning the inverse dynamics of uncertain robotic systems

불확실성이 있는 로봇 시스템의 역모델 학습에 의한 신경회로망 제어

  • Kim, Sung-Woo (Dept. of Electrical Electronic Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Ju-Jang (Dept. of Electrical Electronic Engineering, Korea Advanced Institute of Science and Technology)
  • 김성우 (한국과학기술원 전기 및 전자공학과) ;
  • 이주장 (한국과학기술원 전기 및 전자공학과)
  • Published : 1995.12.01
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Abstract

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

Keywords

References

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