NN Saturation and FL Deadzone Compensation of Robot Systems

로봇 시스템의 신경망 포화 및 퍼지 데드존 보상

  • Jang, Jun-Oh (Uiduk University, Department of Information and Electronic Eng.)
  • 장준오 (위덕대학교 정보전자공학과)
  • Published : 2008.10.31

Abstract

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.

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