Fault Tolerant Flight Control, a Physical Model Approach

Lombaerts, Thomas; Chu, Ping; Mulder, J.A.; Stroosm, Olaf

doi:10.5772/15177

Cited by 39 publications

(35 citation statements)

References 27 publications

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“…It is a relatively challenging subject with low support from the aerospace industry. Industrial end-users are generally more skeptical about FTC bene¦ts, although several successful demonstrations are available [2,3]. The reason is mostly related to the fact that any modi¦cation to §ight control laws is considered to be a very critical technological device which needs very long validation and certi¦cation process.…”

Section: Fault Tolerant Controlmentioning

confidence: 99%

“…The recovery and recon¦guration actions can have di¨erent goals and characteristics depending on the considered system. Fault tolerant control systems seek to provide, at worst, a degraded level of performance in the faulty situations [2,3]. For aerospace vehicles, FTG could provide a greater §exibility for an safe recovery in case of degraded §ight conditions.…”

Section: Introductionmentioning

confidence: 99%

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The challenge of advanced model-based fdir techniques for aerospace systems: the 2011 situation

Zolghadri

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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For aerospace systems, advanced model-based Fault Detection, Identi¦-cation, and Recovery (FDIR) challenges range from predesign and design stages for upcoming and new programs up to the improvement of the performance of in-service §ying systems. However, today, their application to real aerospace world has remained extremely limited. The paper underlines the reasons for a widening gap between the advanced scienti¦c FDIR methods being developed by the academic community and technological solutions demanded by the aerospace industry.

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Section: Fault Tolerant Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The challenge of advanced model-based fdir techniques for aerospace systems: the 2011 situation

Zolghadri

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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“…Measurements of speci¦c forces and angular rates can be corrected for their sensor properties, such as the bias, since it is a part of the state estimator output [31]. Figures 3 and 4 show the time histories of the calculated dimensionless forces and moments coe©cients together with the corresponding trajectory of the simulated engine separation scenario.…”

Section: Creating the Aerodynamic Forces And Momentsmentioning

confidence: 99%

Model-based fault detection and identification with online aerodynamic model structure selection

Lombaerts

2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modi¦cation of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identi¦cation (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identi¦cation algorithm is demonstrated on a simulation data set.

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“…Such double fuzzy set is beneficial in many systems where the exact membership function is very hard to be determined. Therefore, such double fuzzy sets provide additional design degrees of freedom (DOF), which enhances the capability of dealing with the effect of parameter uncertainties, 24 and it has been used widely in many fields, such as face recognition, reconfigurable robots, finance, and so on. Given such advantages, the I-T-II T-S fuzzy modeling method is adopted to handle the nonlinearity and uncertainties of the HFVs longitudinal dynamic system.…”

Section: Introductionmentioning

confidence: 99%

Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults

Wang

Chen

et al. 2017

International Journal of Advanced Robotic Systems

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This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi-Sugeno fuzzy model. An interval type-II Takagi-Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of such method are verified by a series of the comparison of numerical simulation results.

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Fault Tolerant Flight Control, a Physical Model Approach

Cited by 39 publications

References 27 publications

The challenge of advanced model-based fdir techniques for aerospace systems: the 2011 situation

The challenge of advanced model-based fdir techniques for aerospace systems: the 2011 situation

Model-based fault detection and identification with online aerodynamic model structure selection

Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults

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