• Title/Summary/Keyword: 복수모형 확률 설계기법

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Multiple-Model Probabilistic Design for Centralized Repetitive Controllers of Multiple Systems (다물체시스템의 중앙집중 연속학습제어 복수모형 확률설계기법)

  • Lee, Soo-Cheol
    • Journal of Korea Society of Industrial Information Systems
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    • v.16 no.4
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    • pp.99-105
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    • 2011
  • 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.

Multiple-Model Probabilistic Design of Repetitive Controllers (연속반복학습제어의 복수모형 확률설계기법)

  • Lee, Soo-Cheol
    • Journal of Korea Society of Industrial Information Systems
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    • v.13 no.2
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    • pp.1-7
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    • 2008
  • This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the 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 system.

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