• 제목/요약/키워드: Closed-loop State and Input Observer(CSIO)

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입력관측기의 정량적 성능지표 (I) -과도상태 해석- (A Quantitative Performance Input for an Input Observer ( I ) - Analysis in Transient State -)

  • 정종철;이범석;허건수
    • 대한기계학회논문집A
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    • 제26권10호
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    • pp.2060-2066
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    • 2002
  • The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor transient performance with respect to ill-conditioning factors such as unknown initial estimates, round-off error, etc. For the robust transient performance, the effects of these ill-conditioning factors must be minimized in designing observers. In this paper, the transient performance of the closed-loop state and input observer is investigated quantitatively by considering the error bounds due to ill-conditioning factors. The performance indices are selected from these error bounds and are related to the observer robustness with respect to the ill -conditioning factors. The closed-loop state and input observer with small performance indices is considered as a well-conditioned observer from the transient perspective.

입력관측기의 정량적 성능지표 (II) -정상상태 해석- (A Quantitative Performance Index for an Input Observer (II) - Analysis in Steady-State -)

  • 정종철;이범석;허건수
    • 대한기계학회논문집A
    • /
    • 제26권10호
    • /
    • pp.2067-2072
    • /
    • 2002
  • The closed-loop state and input observer is a pole-placement type observer and estimates unknown state and input variables simultaneously. Pole-placement type observers may have poor performances with respect to modeling error and sensing bias error. The effects of these ill-conditioning factors must be minimized for the robust performance in designing observers. In this paper, the steady-state performance of the closed-loop state and input observer is investigated quantitatively and is represented as the estimation error bounds. The performance indices are selected from these error bounds and are related to the robustness with respect to modeling errors and sensing bias. By considering both transient and steady-state performance, the main performance index is determined as the condition number of the eigenvector matrix based on $L_2$-norm.