LPV model-based robust diagnosis of flight actuator faults

Varga, Andreas; Ossmann, Daniel

doi:10.1016/j.conengprac.2013.11.004

Cited by 47 publications

(30 citation statements)

References 24 publications

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“…A new scheduling parameterρ of the quasi-LPV model is defined, such thatρ = ρ x T and sign(ẋ) allow upward and downward movements of the control surfaces to be distinguished. More details of using the above quasi-LPV model in (24) to approximate actuator dynamics in (11) are discussed in Hecker & Pfifer (2014); Varga & Ossmann (2014); Ossmann & Varga (2015). The effect of the control surface disconnection is estimated at the global level and the associated faulty LPV model is given bẏ…”

Section: Benchmark Resultsmentioning

confidence: 99%

“…Recently, the application of linear parameter varying (LPV) concepts to system modelling, control and FDD have also received much attention (Balas, 2002;Bokor & Balas, 2004;Henry, 2008;Sato, 2010;Wei & Verhaegen, 2011a;Hecker & Pfifer, 2014;Alwi & Edwards, 2014;Henry et al, 2014;Varga & Ossmann, 2014;Vanek et al, 2014;Chen et al, 2016;Rodonto et al, 2015;Ossmann & Varga, 2015;Rotondo et al, 2015;Alwi et al, 2015). Nevertheless, the technical demands of model-based FDD, especially for the FDD problem based on using LPV, are still quite limited and restrictive in the aerospace industry (Zolghadri, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The ADDSAFE project benchmark was provided to several academic and industrial partners involved in this project to evaluate the efficiency of their FDD approaches on various fault scenarios (Alwi & Edwards, 2014;Henry et al, 2014;Varga & Ossmann, 2014;Vanek et al, 2014;Van Eykeren & Chu, 2014). The benchmark model is highly representative of a generic twin engine civil commercial aircraft including the nonlinear rigid-body aircraft model with a full set of control surfaces, actuator models, sensor models, flight control laws and pilot inputs.…”

Section: Introductionmentioning

confidence: 99%

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Application of model-based LPV actuator fault estimation for an industrial benchmark

Chen

Patton

Goupil

2016

Control Engineering Practice

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To bridge the gap between model-based fault diagnosis theory and industrial practice, a linear parameter varying H − /H ∞ fault estimation approach is applied to a high fidelity nonlinear aircraft benchmark. The aim is to show how the fault estimation can provide robust early warning of actuator fault detection scenarios that can lead to abnormal aircraft flight configurations. The fault estimator state space solution is parameterized a priori using parameter-independent design freedom. Following this only constant free matrices are determined and the resulting affine linear parameter varying estimator has low computational load. The evaluation uses parametric simulation via an industry standard Monte Carlo campaign supported by a functional engineering simulator. The simulations are carried out in the presence of aerodynamic database uncertainties and measurement errors covering a wide range of the flight envelope.

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Section: Benchmark Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of model-based LPV actuator fault estimation for an industrial benchmark

Chen

Patton

Goupil

2016

Control Engineering Practice

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“…Another advantage is many nonlinear systems can be naturally approximated by LPV systems [13], [14]. 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]).…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

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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“…Model-based fault detection and isolation (FDI) algorithms have been the subject of intensive investigation over the past two decades [10][11][12][13][14]. Furthermore, many schemes considering actuator faults have been proposed in succession, such as the observer-based method [15], the parity space method [16] and the multiple model-based method [17].…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Estimation and Reconfiguration Control for the Quad-Rotor Helicopter

Chen

Lei

Tao

et al. 2016

International Journal of Advanced Robotic Systems

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In this paper, an improved reconfiguration control scheme via an H ∞ fault observer and adaptive control is studied for the quad-rotor helicopter with actuator faults. The bilinear problem is eliminated by constructing fault compensation and control law reconfiguration in the adaptive controller. Fault estimation is achieved by designing the fault observer with an H ∞ performance index, which is applied to evaluate the 'locking in place' fault of the actuator in a quad-rotor helicopter. By drawing the H ∞ performance index into the adaptive fault observer, an asymptotically convergent estimated error can be attained and the burden of the adaptive controller is alleviated. Some simulation and experimental results confirm the availability of the reconfiguration control scheme.

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LPV model-based robust diagnosis of flight actuator faults

Cited by 47 publications

References 24 publications

Application of model-based LPV actuator fault estimation for an industrial benchmark

Application of model-based LPV actuator fault estimation for an industrial benchmark

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

Actuator Fault Estimation and Reconfiguration Control for the Quad-Rotor Helicopter

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