An overview of the FP7 RECONFIGURE project: industrial, scientific and technological objectives

Goupil, Philippe; Boada-Bauxell, Josep; Marcos, Andrés; Rosa, Paulo; Kerr, Murray; Dalbies, Laurent

doi:10.1016/j.ifacol.2015.09.653

Cited by 54 publications

(66 citation statements)

References 7 publications

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“…The simulation results from the Monte Carlo campaign show the robustness of the fault estimation against the parametric uncertainties raised by plant-model mismatch, aerodynamic database uncertainties and physical parameter measurements errors. The future work is to evaluate the proposed scheme in the most recent EU-FP7 RECONFIGURE project (Goupil et al, , 2015 wherein a larger range of flight conditions, more challenging fault detection/fault tolerant control scenarios and various flight manoeuvres including different wind profiles and turbulence are taken into account. …”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

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

Chen

Patton

Goupil

2016

Control Engineering Practice

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“…Since by assumption the set Ω is compact, it can be enclosed by a polytope, and then by determining the vertices of the enclosing polytope, the system in (32) and (36) can be represented as a polytopic system, with vertices β 1 , β 2 , . .…”

Section: Theorem 1 Consider the Observer In (8)-(9) With Gains Parammentioning

confidence: 99%

“…An existing controller has been used to maintain the load factor at the required reference command [32]. The faults in α and V g are assumed to be (a) step signals, occurring at 10 s; (b) oscillatory (sinusoid) signals occurring at 10 s; (c) drift faults which starts from 10 s. The simulations have been performed using the scheme proposed in the paper with imperfect scheduling parameters (ρ).…”

Section: Simulationsmentioning

confidence: 99%

“…An earlier version of this work was presented in [31] and dealt with an actuator fault problem. In [31] all the simulations were performed on simplified LPV models, whereas in this paper, the work has been extended to deal with sensor faults and the simulation results are based on data from a high-fidelity nonlinear aircraft model [32]. The main contributions of this paper are: (a) 1) the systematic development of a SMO for a class of LPV systems to reconstruct faults; 2) the proposed SMO is specifically developed to handle uncertain scheduling parameters; 3) the proposed approach is validated on data from the RECONFIGURE benchmark [32] which represents a high-fidelity nonlinear aircraft model.…”

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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“…During the last few decades, many approaches are proposed to detect and estimate sensor faults by making use of the model information [2,3,4,5]. Projects related to FDI in aerospace engineering, the REconfiguration of CONtrol in Flight for Integral Global Upset REcovery (RECONFIGURE) [6] and Advanced Fault Diagnosis for Sustainable Flight Guidance and Control (ADDSAFE) project [1], also con-faults. Marcos et al [7] and Freeman et al [8] use H ∞ to detect and isolate the sensor faults.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

Kampen

Visser

et al. 2016

Control Engineering Practice

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Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodelled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.

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An overview of the FP7 RECONFIGURE project: industrial, scientific and technological objectives

Cited by 54 publications

References 7 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

Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

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