Data-driven fault estimation of non-minimum phase LTI systems

Chengpu Yu, Michel Verhaegen

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

Many recently developed data-driven fault estimation methods are restricted to minimum-phase systems so that their practical applications are limited. In this paper, the data-driven fault estimation for non-minimum phase (NMP) systems is studied, for which the main difficulty is that the unstable zeros of an NMP system will result in a growing fault-estimation error. To deal with this problem, the inverse of an NMP system is equivalently formulated as a mixed causal and anti-causal system, and the proposed fault estimator is the sum of a stable causal filter and a stable anti-causal filter. The proposed fault estimator is shown to be asymptotically unbiased and its performance is demonstrated by numerical simulations.

Original languageEnglish
Pages (from-to)181-187
JournalAutomatica
Volume92
DOIs
Publication statusPublished - 2018

Keywords

  • Data-driven methods
  • Fault estimation
  • Non-minimum phase systems
  • Subspace identification

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