Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

Yanzheng Zhu, Nuo Xu, Fen Wu, Xinkai Chen, Donghua Zhou

研究成果: Article査読

抄録

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuous-time Markov jump piecewise-affine (PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed H performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation. Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

本文言語English
ページ(範囲)418-429
ページ数12
ジャーナルIEEE/CAA Journal of Automatica Sinica
11
2
DOI
出版ステータスPublished - 2024 2月 1

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 情報システム
  • 制御と最適化
  • 人工知能

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