X-31A system identification using single surface excitation at high angles of attack

Weiß, S.; Friehmelt, H.; Plaetschke, E.; Rohlf, D.

doi:10.2514/6.1995-3436

Cited by 11 publications

(8 citation statements)

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“…9,33 System-identification techniques provided improved results for flight-test planning, flight envelope expansion, and a database for simulation and control law modification and validation. 9,32 These techniques continue to be applied to the successor project X-31 VECTOR now under progress, details of which are left to another dedicated paper in this issue.…”

Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 97%

Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 99%

“…3,32 Analysis of X-31A flight data enabled unique experimental and estimation techniques for unstable systems. 32 Reference 9 provides a brief account of intricacies and subtleties involved and problems encountered in the parameter estimation of an unstable aircraft that can be flown only in closed loop with the aid of highly complex flight-control laws. A variety of algorithms in the time domain (artificially stabilized output-error method, filter-error method, equation decoupling, regression analysis, and extended-Kalman-filter technique) were applied in this case.…”

Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 99%

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Aerodynamic Modeling and System Identification from Flight Data-Recent Applications at DLR

Jategaonkar

Fischenberg

Gruenhagen

2004

Journal of Aircraft

162

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This paper provides an overview of a few selected system-identification activities carried out during the last decade at DLR, the German Aerospace Center. After a brief account of parameter-estimation methods and of consistency checking of recorded flight data, an emphasis is placed on 1) development of global, high-fidelity aerodynamic databases for training simulators; 2) models for in-flight simulator; 3) modeling of stall hysteresis; 4) estimation of aerodynamic flowfield characteristics and validation of aircraft reaction models from wake vortex encounter flights; and 5) a parametric wake distortion model to improve helicopter coupling off-axis response. These examples cover a variety of flight vehicles with widely different characteristics, namely, the military transport aircraft Transall C-160, commuter aircraft Dornier 328, in-flight simulator VFW-614 ATTAS, high-altitude research aircraft G850 Strato 2C, Airbus-type aircraft, fly-by-wire helicopter BO-105, and fly-by-light helicopter EC-135. Besides parameter estimation aspects, those related to model validation are discussed. Through succinct examples it is brought out that system identification has become an indispensable tool to support not only research but also industry activities in various key areas such as model validation, handling qualities evaluation, control law design, and flight-vehicle design and certification. Thus, it contributes significantly to risk and cost reduction.

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Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 97%

Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 99%

Section: X-31a Unstable Aircraft System Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Aerodynamic Modeling and System Identification from Flight Data-Recent Applications at DLR

Jategaonkar

Fischenberg

Gruenhagen

2004

Journal of Aircraft

162

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“…The DLR and Dryden have worked together on PID dating back to at least 1970 and as recently as the mid-1990s during flight tests of the X-31 Enhanced Fighter Maneuverability demonstrator. 34 In the Netherlands, significant PID contributions have also been made by J. A.…”

Section: Outside Drydenmentioning

confidence: 97%

Retrospective and Recent Examples of Aircraft Parameter Identification at NASA Dryden Flight Research Center

Wang

Iliff²

2004

Journal of Aircraft

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A brief retrospective is presented on the development of aircraft aerodynamic parameter identification at NASA Dryden Flight Research Center, tracing its origins from the mid-1960s through recent flight examples. The maximum-likelihood output-error approach is summarized, leaving details to ample references. A partial listing is provided of flight programs at Dryden over the last 40 years that have used parameter identification to support flight testing. In particular, highlights and lessons learned within the last 12 years are described from the X-29A, F-18, SR-71, and space shuttle programs. Results show the usefulness of modeling state noise to account for uncommanded responses caused by separated and unsteady flows at high angles of attack, the importance of proper maneuver planning in reducing data scatter, and the need for multiple and repeated maneuvers to adequately characterize the estimated stability and control derivatives. Over the last 40 years, aircraft parameter identification has proven invaluable to both research and production flight vehicles in safely expanding the operational flight envelope, placard modification and removal, updating the aerodynamic database and math model, refining ground-based simulators, revising control system design, and improving handling qualities.

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“…For situations like these, data partitioning becomes an interesting methodology to obtain the aerodynamic model. 1,2 Common partitioning is made based on ranges of angle of attack 3,4 or sideslip, or a combination of both, 5 although it may be applied to any other independent variable.…”

Section: Introductionmentioning

confidence: 99%

High Angle of Attack Model Identification with Compressibility Effects

Dias¹

2015

AIAA Atmospheric Flight Mechanics Conference

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This work presents an aerodynamic model capable of representing lift, drag and pitch moment coefficients at high angles of attack (AOA), accounting for compressibility effects and aerodynamic hysteresis during stall. The modeling was peformed using flight test data collected during accelerated stalls of a fighter trainer jet, which consisted in wind-up turn (WUT) maneuvers from Mach 0.40 up to 0.73. A nonlinear model structure, based on Kirchhoff 's theory of separated flow, was used to deal with high AOA effects. However, as the aircraft reaches high speeds, the aerodynamic coefficients become a function of Mach number. In order to deal with compressibility effects, the flight data were partitioned based on Mach number intervals, so that a different set of coefficients is applicable to each range of Mach. The values obtained for each coefficient as a function of Mach were mostly compatible with those expected from theory. During the validation tests, using a different set of data, it was clear that a single set of coefficients for a single Mach number was not able to reproduce the flight data in the entire envelope of Mach and AOA tested.

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X-31A system identification using single surface excitation at high angles of attack

Cited by 11 publications

References 0 publications

Aerodynamic Modeling and System Identification from Flight Data-Recent Applications at DLR

Aerodynamic Modeling and System Identification from Flight Data-Recent Applications at DLR

Retrospective and Recent Examples of Aircraft Parameter Identification at NASA Dryden Flight Research Center

High Angle of Attack Model Identification with Compressibility Effects

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