A finite memory observer structure of continuous descriptor systems

Kratz,; Bousghiri, S.; Nuninger, Walter

doi:10.1109/acc.1995.533873

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…transfer functions [6,22], state-space models [5,3,2], or descriptor models e.g. [8,11]. Since DAE models cover all these classes of models, the methods presented in this paper are applicable to all the three previous cases.…”

Section: Introductionmentioning

confidence: 99%

“…and it must therefore hold that N H (s)F i (s) = 0 which is equivalent to (11). (2) there exist also signals x and z such that (1) is fulfilled.…”

Section: Theorem 3 Fault I Is Detectable In (1) If and Only Ifmentioning

confidence: 99%

“…Observer-based approaches have been studied in [8,11,12,13,20]. A limitation of these works compared to the present one is that they all assume observability.…”

Section: Introductionmentioning

confidence: 99%

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Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

Nyberg

Frisk

2006

IEEE Trans. Automat. Contr.

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Linear residual generation for DAE systems has been considered. In all results derived, no distinction between input and output signals is done. A complete characterization and parameterization of all residual generators is presented. Further, a condition for fault detectability in DAE systems is given. Based on the characterization of all residual generators, a design strategy for residual generators for DAE systems is presented. Given that a set of faults are detectable, the design strategy will result in a residual generator sensitive to all the detectable faults. Further the residual generator is guaranteed to be of lowest possible order. Special care has been devoted to assure this property also for non-controllable systems.

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Section: Introductionmentioning

confidence: 99%

“…and it must therefore hold that N H (s)F i (s) = 0 which is equivalent to (11). (2) there exist also signals x and z such that (1) is fulfilled.…”

Section: Theorem 3 Fault I Is Detectable In (1) If and Only Ifmentioning

confidence: 99%

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Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

Nyberg

Frisk

2006

IEEE Trans. Automat. Contr.

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“…The obtained results show that finite memory observer can enhance the opportunity to extract the contained information about incipient faults in recent measurement and then increase the capability of timely fault alarm. Hereafter, such finite memory FD observer was extended to continuous descriptor systems in , where the residual sensitivity to fault was verified through a circuit network simulation. Later, robustness of finite memory FD observer against noises was further illustrated in by comparison with the generalized pseudo‐Bayesian first order approach.…”

Section: Introductionmentioning

confidence: 99%

Robust periodically time‐varying horizon finite memory fault detection filter design for polytopic uncertain discrete‐time systems

Han

2017

Intl J Robust & Nonlinear

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Summary In this paper, a robust periodically time‐varying horizon finite memory fault detection filter (PTVHFM‐FDF) is proposed to generate residual signal for the purpose of system monitoring. The major advantage of PTVHFM‐FDF is that finite historical data can be sufficiently utilized to enhance the robustness of fault detection against model uncertainty and external disturbance. The analysis, design, and application of PTVHFM‐FDF consist of several steps. Firstly, a periodic time‐varying residual error system is constructed on the basis of augmented method and time domain partition method. Secondly, in facilitating robust stability and H∞ performance analysis, the time‐varying residual error system is further transformed into a polyhedral time‐invariant system on the basis of time lifting technique. Thirdly, less conservative PTVHFM‐FDF analysis and design conditions are then obtained on the basis of Finsler relaxation lemma and slack variable structure definition. Fourthly, choices of memory window horizon parameters are properly suggested on the basis of a compromise between FDF robustness and its structural complexity. Finally, an integrated design and application algorithm of PTVHFM‐FDF is summarized. Effectiveness of PTVHFM‐FDF is verified through three examples. Copyright © 2017 John Wiley & Sons, Ltd.

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“…In this paper, residual generation based on linear models is considered. Previous works on linear residual generation has primarily dealt with models on transfer function form (Gertler, 1998;Viswanadham et al, 1987), state space form (Frisk and Nyberg, 2001;Chow and Willsky, 1984;Chen and Patton, 1999), or descriptor form (Hou, 2000;Kratz et al, 1995). In contrast, the key result of this paper is that we consider all models that are described by any set of linear differentialalgebraic equations.…”

Section: Introductionmentioning

confidence: 99%

Residual Generation for Fault Diagnosis of Systems Described by General Linear Differential-Algebraic Equations

Frisk

Nyberg

2002

IFAC Proceedings Volumes

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A finite memory observer structure of continuous descriptor systems

Cited by 5 publications

References 9 publications

Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations

Robust periodically time‐varying horizon finite memory fault detection filter design for polytopic uncertain discrete‐time systems

Residual Generation for Fault Diagnosis of Systems Described by General Linear Differential-Algebraic Equations

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