A high-fidelity airbus benchmark for system fault detection and isolation and flight control law clearance

Goupil, Philippe; Puyou, Guilhem

doi:10.1051/eucass/201306249

Cited by 13 publications

(16 citation statements)

References 4 publications

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“…Realistic sensor and actuator dynamics as well as noise have been included in the simulation [20], [19]. Also note that during the simulation, the mass and center of gravity do not change significantly and therefore are not plotted.…”

Section: B Addsafe Simulation Resultsmentioning

confidence: 99%

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

Alwi

Edwards

2013

Intl J Robust & Nonlinear

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This paper presents a robust FDI scheme using a sliding mode observer based on an LPV system, with fault reconstruction capability. Both actuator and sensor fault reconstruction schemes are considered which possess robustness against a certain class of uncertainty and corrupted measurements. For actuator fault reconstruction, the input distribution matrix (associated with the actuators being monitored) is factorized into fixed and varying components. LMIs are used to design the key observer parameters in order to minimize the effect of uncertainty and measurement corruption on the fault reconstruction signal. The faults are reconstructed using the output error injection signal associated with the nonlinear term of the sliding mode observer. For sensor fault reconstruction, the idea is to re-formulate the problem into an actuator fault reconstruction scenario so that the same design procedure can be applied. This is achieved by augmenting the original system with the filtered sensors being monitored. Simulations using a full nonlinear model of a large transport aircraft are presented, and show good fault reconstruction performance.

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Section: B Addsafe Simulation Resultsmentioning

confidence: 99%

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

Alwi

Edwards

2013

Intl J Robust & Nonlinear

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“…A benchmark system that is highly representative generic model of a twin engine civil commercial aircraft is defined within the ADDSAFE benchmark, which contains the nonlinear aircraft rigid-body model, flight control laws, pilot inputs and a full set of actuator and sensor dynamic models (Goupil & Puyou, 2011).…”

Section: Addsafe Lpv Model In Local and Global Levelsmentioning

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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“…The design freedom associated with the observer are the gains G l (ρ), G n the scalar K(t, y, y, ρ) and the Hurwitz matrixÃ 22 . The modulation scalar K(t, y, u, ρ) is any function chosen so that…”

Section: Lpv Sliding Mode Scheme For Sensor Fault Reconstructionmentioning

confidence: 99%

“…Once L 1 and hence L is determined, the fixed gainG n from (19) is determined. The gainG l (ρ) from (11) (which also depends on L) is parameterized by the fixed Hurwitz matrixÃ 22 . Once these parameters are chosen, the matrix G l (ρ) can described explicitly in terms of a formula involving L,Ã 22 and sub-matrices extracted from A(ρ).…”

Section: Lpv Sliding Mode Scheme For Sensor Fault Reconstructionmentioning

confidence: 99%

“…The paper describes the development process from tuning, to testing and then industrial evaluations of the robust FDD scheme on the ADDSAFE Functional Engineering Simulator (FES) [21]. The results from the initial tuning and testing process are based on the simulation and verification FES which incorporates a high fidelity nonlinear aircraft model developed by AIRBUS [22]. This is intended to provide a design tuning and assessment tool for the proposed fault detection and diagnosis (FDD) schemes developed within the ADDSAFE consortium, and acts as a platform to test the robustness of the proposed schemes using a predetermined grid or sequence to cover a wide range of flight conditions and includes perturbations in the aircraft parameters.…”

Section: Introductionmentioning

confidence: 99%

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Development and application of sliding mode LPV fault reconstruction schemes for the ADDSAFE Benchmark

Alwi

Edwards

2014

Control Engineering Practice

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This paper describes the development and the evaluation of a robust sliding mode observer fault detection scheme applied to an aircraft benchmark problem as part of the ADDSAFE project. The ADDSAFE benchmark problem which is considered in this paper is the yaw rate sensor fault scenario. A robust sliding mode sensor fault reconstruction scheme based on an LPV model is presented, where the fault reconstruction signal is obtained from the so-called equivalent output error injection signal associated with the observer. The development process includes implementing the design using AIRBUS's so-called SAO library which allows the automatic generation of flight certifiable code which can be implemented on the actual flight control computer. The proposed scheme has been subjected to various tests and evaluations on the Functional Engineering Simulator conducted by the industrial partners associated with the ADDSAFE project. These were designed to cover a wide range of the flight envelope, specific challenging manoeuvres and realistic fault types. The detection and isolation logic together with a statistical assessment of the FDD schemes are also presented. Simulation results from various levels of FDD development (from tuning, testing and industrial evaluation) show consistently good results and fast detection times.

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A high-fidelity airbus benchmark for system fault detection and isolation and flight control law clearance

Cited by 13 publications

References 4 publications

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

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

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

Development and application of sliding mode LPV fault reconstruction schemes for the ADDSAFE Benchmark

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