Aeroservoelastic Sensor-Based Control
Aeroservoelastic Sensor-based Control (certifiable-by-design with performance and stability guarantees)
Brenner, Marty↗
Engineering topics
Brenner, Marty
Publications and source records attributed to Brenner, Marty.
Aeroelastic Flight Data Analysis with the Hilbert-Huang Algorithm
This paper investigates the utility of the Hilbert-Huang transform for the analysis of aeroelastic flight data. It is well known that the classical Hilbert transform can be used for time-frequency analysis of functions or signals. Unfortunately, the Hilbert transform can only be effectively applied to an extremely small class of signals, namely those that are characterized by a single frequency component at any instant in time. The recently-developed Hilbert-Huang algorithm addresses the limitations of the classical Hilbert transform through a process known as empirical mode decomposition. Using this approach, the data is filtered into a series of intrinsic mode functions, each of which admits a well-behaved Hilbert transform. In this manner, the Hilbert-Huang algorithm affords time-frequency analysis of a large class of signals. This powerful tool has been applied in the analysis of scientific data, structural system identification, mechanical system fault detection, and even image processing. The purpose of this paper is to demonstrate the potential applications of the Hilbert-Huang algorithm for the analysis of aeroelastic systems, with improvements such as localized/online processing. Applications for correlations between system input and output, and amongst output sensors, are discussed to characterize the time-varying amplitude and frequency correlations present in the various components of multiple data channels. Online stability analyses and modal identification are also presented. Examples are given using aeroelastic test data from the F/A-18 Active Aeroelastic Wing aircraft, an Aerostructures Test Wing, and pitch-plunge simulation.
Brenner, Marty↗
Flight-Test Evaluation of Flutter-Prediction Methods
The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.
Lind, RIck↗
Aerostructures Test Wing
The Aerostructures Test Wing (ATW) was an apparatus used in a flight experiment during a program of research on aeroelastic instabilities. The ATW experiment was performed to study a specific instability known as flutter. Flutter is a destructive phenomenon caused by adverse coupling of structural dynamics and aerodynamics. The process of determining a flight envelope within which an aircraft will not experience flutter, known as flight flutter testing, is very dangerous and expensive because predictions of the instability are often unreliable. The ATW was a small-scale airplane wing that comprised an airfoil and boom (see upper part of Figure 1). For flight tests, the ATW was mounted on the F-15B/FTF-II testbed, which is a second-generation flight-test fixture described in Flight-Test Fixture for Aerodynamic Research (DRC- 95-27), NASA Tech Briefs, Vol. 19, No. 9, September 1995, page 84. The ATW was mounted horizontally on this fixture, and the entire assembly was attached to the undercarriage of the F-15B airplane (see lower part of Figure 1). The primary objective of the ATW project was to investigate traditional and advanced methodologies for predicting the onset of flutter. In particular, the ATW generated data that were used to evaluate a flutterometer. This particular flutterometer is an on-line computer program that uses method analysis to estimate worst-case flight conditions associated with flutter. This software was described in A Flutterometer Flight Test Tool NASA Tech Briefs, Vol. 23, No. 1, January 1999, page 52.
Lind, RIck↗
Nonstationary Dynamics Data Analysis with Wavelet-SVD Filtering
Nonstationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied.
Brenner, Marty↗
Wavelet Applications for Flight Flutter Testing
Wavelets present a method for signal processing that may be useful for analyzing responses of dynamical systems. This paper describes several wavelet-based tools that have been developed to improve the efficiency of flight flutter testing. One of the tools uses correlation filtering to identify properties of several modes throughout a flight test for envelope expansion. Another tool uses features in time-frequency representations of responses to characterize nonlinearities in the system dynamics. A third tool uses modulus and phase information from a wavelet transform to estimate modal parameters that can be used to update a linear model and reduce conservatism in robust stability margins.
Lind, Rick↗
Wavelet Applications for Flight Flutter Testing
Wavelets present a method for signal processing that may be useful for analyzing responses of dynamical systems. This paper describes several wavelet-based tools that have been developed to improve the efficiency of flight flutter testing. One of the tools uses correlation filtering to identify properties of several modes throughout a flight test for envelope expansion. Another tool uses features in time-frequency representations of responses to characterize nonlinearities in the system dynamics. A third tool uses modulus and phase information from a wavelet transform to estimate modal parameters that can be used to update a linear model and reduce conservatism in robust stability margins.
Lind, Rick↗
Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins
Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.
Brenner, Marty↗
Estimation of Modal Parameters Using a Wavelet-Based Approach
Modal stability parameters are extracted directly from aeroservoelastic flight test data by decomposition of accelerometer response signals into time-frequency atoms. Logarithmic sweeps and sinusoidal pulses are used to generate DAST closed loop excitation data. Novel wavelets constructed to extract modal damping and frequency explicitly from the data are introduced. The so-called Haley and Laplace wavelets are used to track time-varying modal damping and frequency in a matching pursuit algorithm. Estimation of the trend to aeroservoelastic instability is demonstrated successfully from analysis of the DAST data.
Lind, Rick↗
Worst-Case Flutter Margins from F/A-18 Aircraft Aeroelastic Data
An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, micron, computes a stability margin which directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The micron margins are robust margins which indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 SRA using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.
Lind, Rick↗
Utilizing Flight Data to Update Aeroelastic Stability Estimates
Stability analysis of high performance aircraft must account for errors in the system model. A method for computing flutter margins that incorporates flight data has been developed using robust stability theory. This paper considers applying this method to update flutter margins during a post-flight or on-line analysis. Areas of modeling uncertainty that arise when using flight data with this method are investigated. The amount of conservatism in the resulting flutter margins depends on the flight data sets used to update the model. Post-flight updates of flutter margins for an F/A-18 are presented along with a simulation of on-line updates during a flight test.
Lind, Rick↗
Subspace Identification with Multiple Data Sets
Most existing subspace identification algorithms assume that a single input to output data set is available. Motivated by a real life problem on the F18-SRA experimental aircraft, we show how these algorithms are readily adapted to handle multiple data sets. We show by means of an example the relevance of such an improvement.
Duchesne, Laurent↗
Methods for in-flight robustness evaluation
The goal of this program was to combine modern control concepts with new identification techniques to develop a comprehensive package for estimation of 'robust flutter boundaries' based on experimental data. The goal was to use flight data, combined with a fundamental physical understanding of flutter dynamics, to generate a prediction of flutter speed and an estimate of the accuracy of the prediction. This report is organized as follows: the specific contributions of this project will be listed first. Then, the problem under study will be stated and the general approach will be outlined. Third, the specific system under study (F- 18 SRA) will be described and a preliminary data analysis will be performed. Then, the various steps of the flutter boundary determination will be outlined and applied to tile F-18 SRA data and others.
Brenner, Marty↗