A hierarchical multiple model adaptive control of discrete-time stochastic systems for sensor and actuator uncertainties

Keigo Watanabe, Spyros G. Tzafestas

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

A hierarchical multiple model adaptive control (MMAC) is described for discrete-time stochastic systems with unknown sensor and actuator parameters, where the decentralized structure consists of a central processor and of m local processors which do not communicate between each other. A major assumption in this study is that the central and any local stations have different knowledge of the hypotheses on the unknown parameters. This leads to a flexible design algorithm for passively adaptive control strategies. Furthermore, the coordinator algorithm in evaluating the global a posteriori probability is relatively simple to implement. The result is applied to the design problem of an instrument failure detection and identification (FDI) system.

Original languageEnglish
Pages (from-to)875-886
Number of pages12
JournalAutomatica
Volume26
Issue number5
DOIs
Publication statusPublished - Sep 1990
Externally publishedYes

Keywords

  • Decentralized control
  • Kalman filters
  • failure detection
  • hierarchical decision making
  • parameter estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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