Modeling of Structural Deflections on a F/A-18 Aircraft Following Flight Flutter Tests by Use of Subspace Identification Method

Mota, S. De Jesus; Nadeau-Beaulieu, Michel; Botez, Ruxandra Mihaela; Brenner, Marty

doi:10.2514/6.2007-6303

Cited by 5 publications

(6 citation statements)

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“…Two additional input types were tested. We introduced, as shown in [8], the nine double products of the inputs, and we also decided to Nine double products Fig. 4 F/A-18 aircraft system identification scheme with 21 inputs and four outputs represented in Simulink using the fuzzy identification scheme.…”

Section: Resultsmentioning

confidence: 99%

“…The nine double products were chosen in previous work [8], where it was found that using all of them improved the precision of the system identification. The six derivatives were chosen because we needed to add more information to our aircraft system identification and because they are very important for aircraft identification from a physical point of view.…”

Section: Resultsmentioning

confidence: 99%

“…In aerospace companies, flight-envelope limits calculated using finite element methods are verified and validated through flutter tests, and because of their complexity (aeroelastic and control knowledge), modern aeroservoelastic aircraft models may be identified by means of flutter tests. In fact, the F/A-18 aircraft model has already been identified and validated using the subspace method [8] from the same flight-test data.…”

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confidence: 99%

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Fuzzy Logic Method Use in F/A-18 Aircraft Model Identification

Kouba

Botez

Boely

2010

Journal of Aircraft

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A mathematical model for controlling the structural deflections of an F/A-18 modified aircraft was determined in the Active Aeroelastic Wing technology program. Five sets of signals from flight flutter tests corresponding to the excited inputs (differential ailerons, collective ailerons, collective stabilizers, differential stabilizers, and rudders) were measured at the NASA Dryden Flight Research Center. Two types of signals were used to build this new model: control deflections (the inputs) and structural deflections (the outputs). The fuzzy logic method was used in identifying the nonlinear aircraft models for 16 flight-test cases, based on Mach numbers (between 0.85 and 1.30) and altitudes (between 5000 and 25,000 ft). To find the best model, we tested a variety of systems with different numbers of inputs or fuzzy logic methods. By comparing the results obtained, we conclude that the best results, in terms of our preestablished specifications, were obtained for the 12-input Sugeno system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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Fuzzy Logic Method Use in F/A-18 Aircraft Model Identification

Kouba

Botez

Boely

2010

Journal of Aircraft

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“…An N -layer network is used with A neurons on the nth layer and B neurons on the (n + 1)th layer (see equation (10)) to explain the LMBP algorithm. From Fig.…”

Section: The Lmbp Methodsmentioning

confidence: 99%

“…The four outputs are the left and right wing positions WING L and WING R , and the left and right trailing edge flap positions TEF L and TEF R . In order to improve the match between the model and the FFT data, the number of inputs was increased by adding nine non-linear inputs of second degree to the six original inputs, and then a total of 15 inputs were obtained [10].…”

Section: Definition Of the Systemmentioning

confidence: 99%

Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods

Boely

Botez

Kouba

2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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The aim of this article is to determine the mathematical model between control deflections and structural deflections in an F/A-18 modified aircraft in the active aeroelastic wing programme. One future application would be the design of a flutter suppression model based on flight flutter tests. Five excited sources were provided by NASA Dryden Flight Research Center from flight flutter tests. These excitations, given by aircraft control surfaces, are: differential and collective ailerons, collective and differential stabilizers, and rudders. The neural network and fuzzy logic algorithms were chosen in order to identify the multi-input multi-output system for the F/A-18 aircraft. One main contribution of this article is the mapping of fuzzy logic algorithm results into neural network data. Then, these methods were applied for the F/A-18 model identification and validation for sixteen flight conditions expressed in terms of Mach numbers variations between 0.85 and 1.30 and altitudes varying between 5000 and 25 000 ft. Accurate results were obtained, expressed in terms of fit coefficients between estimated and measured signals greater than 99 per cent, which allows one to conclude that these new methodologies are very efficient for an aircraft identification and validation.

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