한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제11권5호
  • /
  • Pages.1597-1600
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  • 2010
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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비선형 시스템의 불확실성을 보상하는 신경회로망 제어

Uncertainty-Compensating Neural Network Control for Nonlinear Systems

  • 조현섭 (청운대학교 디지털방송공학과)
  • Cho, Hyun-Seob (Dept. of Digital Broadcasting & Electronic Engineering, Chungwoon Univ.)
  • 투고 : 2009.12.31
  • 심사 : 2010.05.13
  • 발행 : 2010.05.31
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초록

본 논문은 비선형 동적 신경망을 이용하여 직접 제어에 관한 연구이다. RBF 신경망을 이용한 제어입력과 근사화 오차 및 외란의 영향을 제거하기 위한 보조제어 입력으로 구성하였다. 외란이나 근사화 오차에 관계없이 플랜트와 기준모델 사이의 오차가 0이 되도록 하는 알고리즘을 구할 수 있었다. 시뮬레이션 결과는 매우 효과적이며 비선형 시스템의 만족스러운 학습 성능을 증명하였다.

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

키워드

참고문헌

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