Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults

Rupp, D.; Ducard, Guillaume; Shafai, Esfandiar; Geering, Hans P.

doi:10.1109/cdc.2005.1582634

Cited by 36 publications

(25 citation statements)

References 2 publications

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“…Nonlinear dynamics can also be used in the MMAE context using a bank of extended Kalman filters. Like its linear counterparts, the filters represent normal and faulty system models [32,33]. Extended Kalman filters have found extensive application in Li-ion battery equivalent circuit models for state and parameter estimation [25,[34][35][36][37] This study aims at detecting and diagnosing over-charge (OC) and over-discharge (OD) faults in a Li-ion battery using nonlinear model representations.…”

Section: Ieee Transactions On Industrial Electronicsmentioning

confidence: 99%

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Sidhu

Izadian

Anwar

2015

IEEE Trans. Ind. Electron.

235

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Section: Ieee Transactions On Industrial Electronicsmentioning

confidence: 99%

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Sidhu

Izadian

Anwar

2015

IEEE Trans. Ind. Electron.

235

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“…where ΔA(x) and ΔB(x) represent the model mismatch, e.g, the difference between our model and Morelli's model [1] due to (6), and Ew denotes the effect due to the small exogenous gust/noise/disturbance with E =ĒΔT ,Ē T = I 3 0 3×6 , where I 3 is a 3-dimensional identity matrix and 0 3×6 is a 3×6 zero matrix. Note that because of the physical limitation of sensor equipments, the measured state x may not be accurate, especially for η = [V, β, α] T .…”

Section: Discretized Modelmentioning

confidence: 99%

“…However, the fault detection and isolation (FDI) problem of the nonlinear flight dynamics model remains an open research problem. There exist several approaches to this problem, such as the extended Kalman filter [3], the higher-order sliding mode observer [4] and the multiple-model adaptiveestimation (MMAE) method [5,6]. In this paper, we introduce a new FDI scheme based on a gain-varying unknown input observer (UIO) approach.…”

Section: Introductionmentioning

confidence: 99%

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

Lum

Loh

2010

J. Control Theory Appl.

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A discrete gain-varying unknown input observer (UIO) method is presented for actuator fault detection and isolation (FDI) problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.

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“…Such an FDI system is detailed in [1] and [2]. It is based on the extended multiple model adaptive estimation method (EMMAE).…”

Section: B Fdi Without Control-surface Deflection Sensormentioning

confidence: 99%

Evaluation of Reduction in the Performance of a Small UAV After an Aileron Failure for an Adaptive Guidance System

Ducard

Kulling

Geering

2007

2007 American Control Conference

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After an actuator failure, the performance of an aircraft is degraded. If a fault detection and isolation system is available in the flight control system, the knowledge of the failure can be used to evaluate the new aircraft performance. Based thereon, a supervision system decides whether the mission can still be continued or if it should be aborted and have the aircraft redirected to the base station. In both cases, the aircraft should still be guided along a trajectory that is compatible with the new flying properties of the airplane. This paper focuses on an aileron failure and shows how the degraded flying performance can be evaluated and used to reconfigure the guidance system. Simulation results show that if the reduced performance due to the actuator failure is taken into account, the safety of the mission is improved.

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Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults

Cited by 36 publications

References 2 publications

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

Evaluation of Reduction in the Performance of a Small UAV After an Aileron Failure for an Adaptive Guidance System

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