한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
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다물체시스템의 중앙집중 연속학습제어 복수모형 확률설계기법

Multiple-Model Probabilistic Design for Centralized Repetitive Controllers of Multiple Systems

  • 이수철 (대구대학교 자동차산업기계공학부)
  • 투고 : 2011.02.17
  • 심사 : 2011.09.07
  • 발행 : 2011.12.30
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초록

본 논문은 다물체 복합시스템 인자의 변위에 강인한 중앙집중식 연속반복학습제어기(Repetitive Controller, RC)를 설계하는 방법을 소개하고자 한다. 이때 사용되는 불확실 인자들은 확률분포함수에 의해 무작위로 설정되게 된다. 분포함수를 직접 적용하는 대신, 본 제어기는 설정된 확률함수로부터 생성된 모형을 기본으로 설계하였다. 이러한 중앙집중식 복수모형 설계기법으로 엄의의 분포함수로 구성된 수많은 불확실 인자들을 다룰 수 있다. 그러므로, 제어기는 반복영역에서 수렴성을 보장하는 비용함수를 주파수영역에서 최소화함으로써 유도할 수 있다. 다물체 복합시스템에서 중앙집중식 복수모형설계 기법을 단수모형 설계기법과 함께 제안하였다.

This paper presents a method to design a centralized repetitive controller that is robust to variations in the multiple system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the centralized repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. Furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the multiple system.

키워드

참고문헌

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