Robust fault reconstruction for linear parameter varying systems using sliding mode observers

Alwi, Halim; Edwards, Christopher

doi:10.1002/rnc.3009

Cited by 95 publications

(98 citation statements)

References 45 publications

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“…The structure of G n associated with the nonlinear injection term is assumed, as in [20], to have the form…”

Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

“…In the LPV approach the gains are automatically scheduled with respect to the plant varying parameters. Another advantage is many nonlinear systems can be naturally approximated by LPV systems [13] [20]). However in all these papers the scheduling parameters are assumed to be perfectly known.…”

Section: Introductionmentioning

confidence: 99%

“…Recent results on LPV based fault detection schemes appears in [15], [16], [17] from work in the EU-funded ADDSAFE project. Recently several sliding mode observer structures have been proposed for LPV systems (see for example [18], [19], [20]). However in all these papers the scheduling parameters are assumed to be perfectly known.…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

“…This exploits the fact that sliding mode observers have an inherent ability for fault reconstruction, due to the unique property of the equivalent injection signal [11]. This work is a direct extension of [20], where it was assumed that the scheduling parameters are perfectly measurable. An earlier version of this work was presented in [31] and dealt with an actuator fault problem.…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

“…The vector ρ ∈ Ω ⊂ R r denotes the varying scheduling parameters, f i (t) ∈ R q models the unknown actuator faults, ξ(t, y, u) ∈ R k captures the uncertainty in the system and the signal d(t) represents an ever-present corruption of the true measured outputs but does not represent sensor noise. As in [20], three assumptions will be made: …”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

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Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

Chandra¹,

Alwi

Edwards

2017

European Journal of Control

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This paper presents a sliding mode fault detection scheme for linear parameter varying (LPV) systems with uncertain or imperfectly measured scheduling parameters. In the majority of LPV systems, it is assumed that the scheduling parameters are exactly known. In reality due to noise or possibly faulty sensors, it is sometimes impossible to have accurate knowledge of the scheduling parameters and a design based on the assumption of perfect knowledge of the scheduling parameters cannot be guaranteed to work well in this situation. This paper proposes a sliding mode observer scheme to reconstruct actuator and sensor faults in a situation where the scheduling parameters are imperfectly known. The efficacy of the approach is demonstrated on simulation data taken from the nonlinear RECONFIGURE benchmark model . One approach to extending linear methods to make them work effectively across the whole plant operating range is to design (linear) observers at select operating points, and then schedule the gains with respect to certain parameters [4]. One of the main difficulties with gain scheduling is the selection of the operating points and how to choose the gains at intermediate points. Furthermore, formally, between the operating points the stability of the observer cannot be guaranteed.A more rigorous alternative approach is based on so-called linear parameter varying (LPV) system designs. In the LPV approach the gains are automatically scheduled with respect to the plant varying parameters. Another advantage is many nonlinear systems can be naturally approximated by LPV systems [13] [20]). However in all these papers the scheduling parameters are assumed to be perfectly known. However in reality this may not be the case: for example inaccurate sensors and/or faults can lead to imperfect knowledge of the parameters. The use of this corrupted information will affect the performance of the observer. The design of Luenberger-like observers in scenarios involving uncertain scheduling parameters has been investigated. In [21], using a Linear matrix inequality (LMI) framework an LPV observer was proposed. Later, a controller and a combined controllerobserver scheme for inexact continuous LPV polytopic systems was presented in [22], [23]. In [24], [25] H ∞ filters were employed to deal with uncertainty in the scheduling parameters; exploiting parameter dependent LMI methods. More recently, an H ∞ filter has been proposed to deal with both additive and multiplicative uncertainties in the LPV parameters in [26]. Recent applications of these ideas to aerospace systems have been explored in [27], [28]. A fault reconstruction scheme for LPV systems with perfect scheduling parameter knowledge, using H ∞ methods has been investigated in [29]. Very few papers have addressed specifically the fault reconstruction problem for LPV systems with uncertain scheduling parameters, notable exceptions are [30]. To the authors' knowledge,

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“…The structure of G n associated with the nonlinear injection term is assumed, as in [20], to have the form…”

Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

See 3 more Smart Citations

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

Chandra¹,

Alwi

Edwards

2017

European Journal of Control

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Adaptive sliding mode–based diagnosis of actuator faults for LPV systems

Rahme

Meskin

Mohammadpour

2017

Adaptive Control & Signal

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In this paper, an adaptive sliding mode observer is developed for actuator fault diagnosis of linear parameter-varying systems. The main advantage of the proposed approach is its ability to cope with time-varying distribution matrix in linear parameter-varying systems. Furthermore, the proposed adaptive observer is characterized by its output robustness against parameter uncertainties and disturbances without any a priori knowledge about their bounds. The efficiency of the proposed fault diagnosis approach is validated using simulation studies.

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Simultaneous actuator and sensor fault estimation for discrete‐time Lipschitz nonlinear systems in finite‐frequency domain

Yang

2017

Optim Control Appl Methods

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SummaryThis paper addresses the problem of actuator and sensor fault reconstruction simultaneously with finite-frequency specifications for discrete-time Lipschitz nonlinear systems. First, an augmented system description is given by extending the sensor fault as an auxiliary state vector. Then, a multiobjective unknown input proportional-integral observer is designed, and the nonlinear error dynamics are transformed into a linear parameter-varying system based on the use of a reformulated Lipschitz property. Sufficient conditions for the design of a fault estimator with 2 finite-frequency H ∞ performances are derived in terms of linear matrix inequalities where a slack variable is introduced to reduce the conservativeness of the design. The proposed method can provide less restrictive linear matrix inequality conditions and get a better fault estimation performance than the existing one in the full frequency domain. Simulation results are given to show the effectiveness of the proposed techniques.

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Robust fault reconstruction for linear parameter varying systems using sliding mode observers

Cited by 95 publications

References 45 publications

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

Adaptive sliding mode–based diagnosis of actuator faults for LPV systems

Simultaneous actuator and sensor fault estimation for discrete‐time Lipschitz nonlinear systems in finite‐frequency domain

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