閉ループ特性に着目した強安定予測制御系

Translated title of the contribution: Strongly Stable Generalized Predictive Control Focused on Closed-loop Characteristics

岡崎 聡, 西崎 純基, Akira Yanou, Mamoru Minami, 〓 明聡

Research output: Contribution to journalArticle

Abstract

This paper proposes a design method of strongly stable generalized predictive control (GPC) focused on closed-loop characteristics. GPC can be extended by coprime factorization, and the extended controller can be designed to be stable by selecting newly introduced parameter. That is, strongly stable system, which means both the closed-loop system and its controller are stable, can be obtained. Although the authors have considered the design method of strongly stable system using coprime factorization, the steady state of output has not been considered when feedback loop was cut. In the case that the controlled plant is stable, the steady state output of strongly stable system is stable even if feedback loop is cut. But for safety, it is desirable that the steady state of output becomes as close to the steady state of closed-loop output as possible even if feedback loop was cut. Therefore this paper explores a design method of strongly stable GPC focused on closed-loop characteristics by algebraic calculation of newly introduced parameter in the extended GPC. The proposed method has the feature that the steady state of output becomes the same as the steady state of closed-loop output even if feedback loop is cut.
Translated title of the contributionStrongly Stable Generalized Predictive Control Focused on Closed-loop Characteristics
Original languageJapanese
Pages (from-to)317-325
Number of pages9
JournalTransactions of the Society of Instrument and Control Engineers
Volume47
Issue number7
DOIs
Publication statusPublished - Jul 31 2011

Keywords

  • generalized predictive control
  • strongly stable
  • coprime factorization

Fingerprint

Dive into the research topics of 'Strongly Stable Generalized Predictive Control Focused on Closed-loop Characteristics'. Together they form a unique fingerprint.

Cite this