Adaptive Neural Control of Nonlinear Pure-feedback Systems

완전궤환 비선형 계통에 대한 적응 신경망 제어기

  • 박장현 (목포대학교 전기제어신소재공학부) ;
  • 김성환 (목포대학교 제어로봇공학과) ;
  • 장영학 (목포대학교 전기제어신소재공학부)
  • Received : 2010.09.04
  • Accepted : 2010.09.28
  • Published : 2010.09.30

Abstract

A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

본 논문은 논어파인 완전궤환 비선형시스템에 대한 적응 신경망 제어기를 제안한다. 제안하는 방법은 원래의 계통식을 새롭게 정의되는 상태변수에 대해서 표준 정규식으로 재구성하여 제어식을 구성함으로써 기존의 알고리듬에서 일반적으로 사용하는 백스태핑(backstepping)을 회피할 수 있다는 장점을 갖는다. 백스태핑을 회피함으로서 본 논문에서 제안하는 새로운 방법은 기존의 제어 알고리듬에 비해서 제어기 구조와 안정도 증명이 현저히 간략해 지게 되는 장점을 갖는다. 제안된 제어기는 미지의 이상 제어식을 근사화하기 위해서 오직 하나의 신경망만을 사용하며 이점 역시 기존의 제어알고리듬이 다수의 신경망을 사용하는 것과 구별되는 점이다. 모의실험을 통하여 제안된 알고리듬의 성능과 효율을 보인다.

Keywords

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