Recursive output-only subspace identification for in-flight flutter monitoring (original) (raw)
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AIAA Atmospheric Flight Mechanics Conference and Exhibit, 2007
The aim of this paper is to determine the mathematical relationship (model) between control deflections and structural deflections of the F/A-18 modified aircraft in the Active Aeroelastic Wing AAW technology program. Five sets of signals from flight flutter tests corresponding to the excited sources were provided by NASA DFRC (Dryden Flight Research Center). These excitations are: differential ailerons, collective ailerons, collective stabilizers, differential stabilizers, and rudders. The signals are of 2 types: control deflections time history data and the corresponding structured deflections. We choose to use the subspace identification method in order to identify the MIMO (Multi Input, Multi Output) system. Nonlinear inputs are used to fit the outputs signals. We apply this method for sixteen flight conditions where Mach number varies from 0.85 to 1.30 and altitudes from 5,000 ft to 25,000 ft. We obtain good results with a fit between the estimated and the measured signals and a correlation factor higher than 90%.
Identification of Flutter Derivatives of Bridge Decks by Stochastic Subspace Method
2009
Flutter derivatives are the essential parameters in the estimations of the critical wind velocity for flutterinstability and the responses of long-span cable supported bridges. These derivatives can be experimentally estimated from wind tunnel tests results. Most of previous studies have used deterministic system identification techniques, in which buffeting forces and responses are considered as noises. In this paper, the covariancedriven stochastic subspace identification technique (SSI-COV) was presented to extract the flutter derivatives of bridge decks from the buffeting test results. An advantage of this method is that it considers the buffeting forces and responses as inputs rather than as noises. The Industrial Ring Road (IRR) cable-stayed bridge crossed Chao Phraya River with main span of 398m was applied for 1:90 scale sectional model test in TU-AIT wind tunnel test as the study case. Wind tunnel tests were performed for four section bridge models, i.e. original section, f...
American Journal of Engineering and Applied Sciences, 2009
Flutter derivatives are the essential parameters in the estimations of the critical wind velocity for flutterinstability and the responses of long-span cable supported bridges. These derivatives can be experimentally estimated from wind tunnel tests results. Most of previous studies have used deterministic system identification techniques, in which buffeting forces and responses are considered as noises. In this paper, the covariancedriven stochastic subspace identification technique (SSI-COV) was presented to extract the flutter derivatives of bridge decks from the buffeting test results. An advantage of this method is that it considers the buffeting forces and responses as inputs rather than as noises. The Industrial Ring Road (IRR) cable-stayed bridge crossed Chao Phraya River with main span of 398m was applied for 1:90 scale sectional model test in TU-AIT wind tunnel test as the study case. Wind tunnel tests were performed for four section bridge models, i.e. original section, fairing-modified section, soffit plate modified section, and combination of those two modified section. The results found that the original section result in high vortex-shedding response and lead to a single torsional flutter at high wind velocity. The results also indicated that the combined fairing and soffit plate modified section is the most aerodynamic shape.
2016
System diagnostics based on vibroacoustics signals, carried out by means of stochas-tic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diag-nostic methods according to data, leading to the generation of residuals allowing fail-ure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine. Keyworda: system diagnostic, subspace method, vibroacoustic.
Performance of subspace based state-space system identification methods
1993
Traditional prediction-error techniques for multivariable system identi cation require canonical descriptions using a large number of parameters. This problem can be avoided using subspace based methods, since these estimate a state-space model directly from the data. The main computations consist of a QR-decomposition and a singular-value decomposition. Herein, a subspace based technique for identifying general nite-dimensional linear systems is presented and analyzed. The technique applies to general noise covariance structures. Explicit formulas for the asymptotic pole estimation error variances are given. The proposed method is found to perform comparable to a prediction error method in a simple example.
Subspace algorithms for system identification and stochastic realization
1991
The subspace approach for linear realization and identi cation problems is a promising alternative for the 'classical' identi cation methods. It has advantages with respect to structure determination and parametrization of linear models, is computationally simple and numerically robust. A summary is given of existing techniques based upon the singular value and qr decomposition. Algebraic, geometric, statistical and numerical points are emphasized. A new idea is outlined for the joint stochastic realization -deterministic identi cation problem. Several examples are given.
IFAC Proceedings Volumes, 1992
Accurate Control of Large Space Structures (LSS) for pointing and tracking applications requires accurate knowledge of the vi brational characteristics of the LSS. Since it is not practical to duplicate the space environment during ground vibration tests, accurate models of LSS can only be obtained via System Identification algorithms applied to real data collected from the LSS after deployment in orbit. A similar problem arises in Aircraft Flutter Testing, which is done to verify that the flight envelop does not contain any aeroelastic or aeroservoelastic instabilities. In this paper, we show that System Identification cap. be done with high accuracy using State Space Models and a Stochastic Realization Algorithm (SRA). It is shown that the Stochastic Realization Algorithm (SRA) outperforms other available techniques and produces excellent results for the AFAL Flexible GRID test data and the X-29 Aircraft Flutter data under natural turbulence conditions. SRA also produces robust estimates of fr equencies, dampings and mode shapes for repeated frequency modes of an elastic membrane even under conditions of 300% multiplicative noise. SRA identifies a multi-input multi-{)utput stochastic state space model for the data using a noniterative technique based on Singular Value Decomposition of a Hankel matrix.