Parameter estimation triggered by continuous-time parity equations

Höfling, Thomas; Isermann, Rolf

doi:10.1109/acc.1995.520926

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…Notice that the derivatives of the rational transfer function matrix F ðz; pÞ are also rational matrices; these multiplied with the input vector uðtÞ yield time-function vectors. Similarly, for the polynomial linear residual e n ðtÞ in (28) the Jacobian is m n pÁj ðtÞ ¼ @Gðz; pÞ @p j uðtÞ À @hðz; pÞ @p j yðtÞ ð 100Þ…”

Section: Parametric Faults In Parity Relationsmentioning

confidence: 99%

“…System parameter estimation is a natural approach to the detection and isolation of parametric faults Reference [2]. Here, we will describe a method which estimates the changes in the physical parameters and will show how it is related to the parity relation approach Refereces [25][26][27][28].…”

Section: Estimation Of Parameter Changesmentioning

confidence: 99%

“…where F 0 ðtÞ is a (sparse) regression matrix and y 0 contains the actual values of the model parameters for the entire system. If there is a fault then (112) applied to the nominal parameter values returns a nonzero residual (prediction error) e n ðtÞ ¼ yðtÞ À F 0 ðtÞy ¼ F 0 ðtÞDy ð113Þ which is the same as e n ðtÞ in (28). The least-squares estimate of the parameters y 0 ; using a moving window of K observations (rather than exponential forgetting, for better response to sudden faults), is obtained as # y y ¼ ½C 0 ðtÞ CðtÞ À1 C 0 ðtÞY ðtÞ ð 114Þ…”

Section: Estimation Of Parameter Changesmentioning

confidence: 99%

See 2 more Smart Citations

All linear methods are equal—and extendible to (some) nonlinearities

Gertler

2002

Intl J Robust & Nonlinear

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SUMMARYSeveral linear methods of residual generation for fault detection and diagnosis are reviewed. The parity relation approach is introduced in some detail, for both additive and parametric faults. The Chow-Willsky scheme, various diagnostic observers and principal component analysis are compared to the additive version. The 'local approach' and the least-squares estimation of parameter changes are shown to be related to the parametric variant. Nonlinear extensions are demonstrated for all the techniques under additive faults.

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Section: Parametric Faults In Parity Relationsmentioning

confidence: 99%

Section: Estimation Of Parameter Changesmentioning

confidence: 99%

Section: Estimation Of Parameter Changesmentioning

confidence: 99%

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All linear methods are equal—and extendible to (some) nonlinearities

Gertler

2002

Intl J Robust & Nonlinear

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“…The multiplicative fault scenario is much less well studied than the additive approach. Alcorta- Garcia et al (1999) studied multiplicative fault isolation in linear systems, and Hofling et al (1995) did parameter estimation triggered by continuous-time parity equations [8,9].…”

Section: Introductionmentioning

confidence: 99%

Model-based fault detection and isolation in steer-by-wire vehicle using sliding mode observer

Ozaki

Yeu

et al. 2009

J Mech Sci Technol

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Steer-by-Wire system (SbW), in which the conventional mechanical linkages between the steering wheel and the front wheel are removed, is suited to active steering control, improving vehicle stability, dynamics and maneuverability. And SbW is implemented to autonomous steering control to assist the driver. However, the SbW vehicle contains unsolved important problems about fault tolerant function. For example, it is the detection of sensor fault and multiplicative fault simultaneously. Fault detection and isolation (FDI) is essential in fault-tolerant problems, and conventional FDI for SbW was based on Kalman filter. But this method has weak robustness and cannot detect sensor fault and multiplicative fault simultaneously. We propose a novel model-based fault detection and isolation method using sliding mode observer in the SbW vehicle, which contains measurement of sensor fault and multiplicative fault. The effectiveness of the proposed method is verified by simulations.

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“…Authors in [9] states that parameter estimation provide deeper insight into the system dynamic and thus make the fault diagnosis easier and in many cases changes in model parameters are directly related to system internal faults. Thus parameter estimation approach is especially suitable for detection of the multiplicative faults [1].…”

Section: Introductionmentioning

confidence: 99%

Fault detection using adaptive observer for a class of nonlinear systems: An LMI approach

Pourgholi

Majd

2013

2013 International Conference on Control Communication and Computing (ICCC)

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This paper addresses a new technique for fault detection and estimation in Lipschitz nonlinear systems influenced by bounded disturbances. A robust adaptive nonlinear observer is designed for this purpose, the stability conditions of this robust adaptive observer based on Lyapunov technique are derived using linear matrix inequality approach.A numerical example is provided which the nonlinear terms depend on unmeasured state and simulation results are presented to show the effectiveness of proposed method.

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Parameter estimation triggered by continuous-time parity equations

Cited by 11 publications

References 6 publications

All linear methods are equal—and extendible to (some) nonlinearities

All linear methods are equal—and extendible to (some) nonlinearities

Model-based fault detection and isolation in steer-by-wire vehicle using sliding mode observer

Fault detection using adaptive observer for a class of nonlinear systems: An LMI approach

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