Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions

Nikolaos Moustakis, Bingyu Zhou, Thuan Le quang, Simone Baldi

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
9 Downloads (Pure)

Abstract

This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state-of-the-art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max-form representation and we exploit the recursive Newton-Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.

Original languageEnglish
Pages (from-to)980-993
JournalInternational Journal of Adaptive Control and Signal Processing
Volume32
Issue number7
DOIs
Publication statusPublished - 2018

Keywords

  • Fault detection and identification
  • Online parameter estimation
  • Piecewise affine unknown systems
  • Unknown partitions

Fingerprint Dive into the research topics of 'Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions'. Together they form a unique fingerprint.

  • Cite this