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access icon free State estimation and fault reconstruction with integral measurements under partially decoupled disturbances

  • Author(s): Yang Liu 1 ; Zidong Wang 2 ; Donghua Zhou 1, 3
    • View affiliations
  • Source: Volume 12, Issue 10, 03 July 2018, p. 1520 – 1526
    DOI:  10.1049/iet-cta.2017.1119 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 14/10/2017, Accepted 12/03/2018, Revised 25/12/2017, Published 15/03/2018

This study is concerned with the state estimation and fault reconstruction problems for a class of discrete systems with integral measurements under partially decoupled disturbances. The considered integral measurements, as functions of the system states over a period of time, reflect the interval time between sample collections and real-time signal processing. Moreover, the process disturbances are allowed to be partially decoupled in the observer design. An augmented state vector is constructed, which consists of the current system state, the delayed system state and the additive fault, and the resultant augmented system is described in a singular form. Then, an unknown input observer is obtained that decouples partial disturbances and attenuates the effect from the remaining undecouplable disturbances. The existence conditions of the desired observer are thoroughly investigated and an algorithm for designing the observer gains is also provided. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Inspec keywords: observers; discrete systems; control system synthesis; fault diagnosis

Other keywords: interval time; delayed system state; system states; process disturbances; discrete systems; decouples partial disturbances; augmented state vector; real-time signal processing; current system state; observer design; fault reconstruction problems; state estimation; partially decoupled disturbances; unknown input observer; considered integral measurements

Subjects: Discrete control systems; Algebra; Simulation, modelling and identification; Control system analysis and synthesis methods

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