대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)

대한전기학회 (The Korean Institute of Electrical Engineers)
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이족 로봇을 위한 자기 회귀 신경 회로망 기반 슬라이딩 모드 제어

Self-Recurrent Neural Network Based Sliding Mode Control of Biped Robot

  • 이신호 (연세대학교 전기전자 공학과) ;
  • 박진배 (연세대학교 전기전자 공학과) ;
  • 최윤호 (경기대학교 전자 공학부)
  • Lee, Sin-Ho (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Park, Jin-Bae (Department of Electrical & Electronic Engineering, Yonsei University) ;
  • Choi, Yoon-Ho (School of Electronic Engineering, Kyonggi University)
  • 발행 : 2006.07.12
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초록

In this paper, we design a robust controller of biped robot system with uncertainties, using recurrent neural network. In our proposed control system, we use the self-recurrent wavelet neural network (SRWNN). The SRWNN makes up for the weak points in wavelet neural network(WNN). While the WNN has fast convergence ability, it dose not have a memory. So the WNN cannot confront unexpected change of the system. However, the SRWNN, having advantage of WNN such as fast convergence, can easily encounter the unexpected change of the system. For stable walking control of biped robot, we use sliding mode control (SMC). Here, uncertainties are predicted by SRWNN. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out computer simulations with a biped robot model to verify the effectiveness of the proposed control system,.

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