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  • 标题:Robust Anomaly Detection Based on a Dynamical Observer for Continuous Linear Roesser Systems
  • 本地全文:下载
  • 作者:Hamid Alikhani ; Mahdi Aliyari Shoorehdeli ; Nader Meskin
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:664-669
  • DOI:10.1016/j.ifacol.2020.12.812
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractMonitoring of industrial systems for anomalies such as faults and cyber-attacks as unknown and extremely undesirable inputs in the presence of other inputs (like disturbances) is an important issue for ensuring the safety and the reliability of their operation. In this study, a robust anomaly detection filter is proposed for continuous linear Roesser systems using dynamic observer framework. Sufficient conditions for the existence of the observer and its sensitivity to anomaly as well as its robustness to disturbances are addressed via linear matrix inequalities (LMIs). The mentioned sensitivity and robustness are based on theH_andH∞performance indices, respectively. Finally, the performance of the proposed observer is demonstrated through a numerical example.
  • 关键词:KeywordsFault detectiondiagnosisAnomaly Detection2D-Roessor SystemsDynamic Observer
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