Aeroservoelastic Model Validation and Test Data Analysis of the F/a-18 Active Aeroelastic Wing


Book Description

Model validation and flight test data analysis require careful consideration of the effects of uncertainty, noise, and nonlinearity. Uncertainty prevails in the data analysis techniques and results in a composite model uncertainty from unmodeled dynamics, assumptions and mechanics of the estimation procedures, noise, and nonlinearity. A fundamental requirement for reliable and robust model development is an attempt to account for each of these sources of error, in particular, for model validation, robust stability prediction, and flight control system development. This paper is concerned with data processing procedures for uncertainty reduction in model validation for stability estimation and nonlinear identification. F/A-18 Active Aeroelastic Wing (AAW) aircraft data is used to demonstrate signal representation effects on uncertain model development, stability estimation, and nonlinear identification. Data is decomposed using adaptive orthonormal best-basis and wavelet-basis signal decompositions for signal denoising into linear and nonlinear identification algorithms. Nonlinear identification from a wavelet-based Volterra kernel procedure is used to extract nonlinear dynamics from aeroelastic responses, and to assist model development and uncertainty reduction for model validation and stability prediction by removing a class of nonlinearity from the uncertainty. Brenner, Martin J. and Prazenica, Richard J. Armstrong Flight Research Center NASA/TM-2003-212021, H-2526, NAS 1.15:212021




Robust Aeroservoelastic Stability Analysis


Book Description

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, . . . . , new challenges. Much of this deVelopment work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The high performance control systems applications in aerospace and astronautics almost have a tradition of exploiting the most advanced control theoretical developments first. The optimal control and ffitering paradigm associated with the names of Kalman, Bucy, Anderson and Moore found application in the astronautics of the 1960'S and 1970'S. At the beginning of the 1980'S, control theory moved on to robustness, singular values and mu-analysis. This new work was associated with the names of Zames, Doyle, Glover, Balas among others. The Advances in Industrial Control monograph series have published several volumes over the years which have archived the applications experience garnered from applying robust control to the aerospace sector problems. Rick Lind and Marty Brenner add to this set with their volume on robust aeroservoelastic stability. This volume reports the application of the structured singular value to aeroelastic and aeroservoelastic aerospace problems.










Aeroservoelasticity


Book Description

This monograph presents the state of the art in aeroservoelastic (ASE) modeling and analysis and develops a systematic theoretical and computational framework for use by researchers and practicing engineers. It is the first book to focus on the mathematical modeling of structural dynamics, unsteady aerodynamics, and control systems to evolve a generic procedure to be applied for ASE synthesis. Existing robust, nonlinear, and adaptive control methodology is applied and extended to some interesting ASE problems, such as transonic flutter and buffet, post-stall buffet and maneuvers, and flapping flexible wing. The author derives a general aeroservoelastic plant via the finite-element structural dynamic model, unsteady aerodynamic models for various regimes in the frequency domain, and the associated state-space model by rational function approximations. For more advanced models, the full-potential, Euler, and Navier-Stokes methods for treating transonic and separated flows are also briefly addressed. Essential ASE controller design and analysis techniques are introduced to the reader, and an introduction to robust control-law design methods of LQG/LTR and H2/H∞ synthesis is followed by a brief coverage of nonlinear control techniques of describing functions and Lyapunov functions. Practical and realistic aeroservoelastic application examples derived from actual experiments are included throughout. Aeroservoelasiticity fills an important gap in the aerospace engineering literature and will be a valuable guide for graduate students and advanced researchers in aerospace engineering, as well as professional engineers, technicians, and test pilots in the aircraft industry and laboratories.







Using System Identification to Compare Global and Local Aerodynamic Modeling from Flight Data


Book Description

A method for identifying and comparing a longitudinal global aerodynamic model to a longitudinal local aerodynamic model for UTSI’s Piper Saratoga aircraft is explained and demonstrated. Large amplitude piloted inputs were used to estimate global nonlinear aerodynamic models from flight data. Flight derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for the longitudinal dimensional force and moment. The results from the global aerodynamic modeling were compared to local linear aerodynamic modeling results gathered with traditional small amplitude doublet inputs. The results from large amplitude piloted inputs compared favorably with small amplitude piloted inputs by ten percent in almost all cases and in significantly less test time.




Wavelet Analyses of F/A-18 Aeroelastic and Aeroservoelastic Flight Test Data


Book Description

Time-frequency signal representations combined with subspace identification methods were used to analyze aeroelastic flight data from the F/A-18 Systems Research Aircraft (SRA) and aeroservoelastic data from the F/A-18 High Alpha Research Vehicle (HARV). The F/A-18 SRA data were produced from a wingtip excitation system that generated linear frequency chirps and logarithmic sweeps. HARV data were acquired from digital Schroeder-phased and sinc pulse excitation signals to actuator commands. Nondilated continuous Morlet wavelets implemented as a filter bank were chosen for the time-frequency analysis to eliminate phase distortion as it occurs with sliding window discrete Fourier transform techniques. Wavelet coefficients were filtered to reduce effects of noise and nonlinear distortions identically in all inputs and outputs. Cleaned reconstructed time domain signals were used to compute improved transfer functions. Time and frequency domain subspace identification methods were applied to enhanced reconstructed time domain data and improved transfer functions, respectively. Time domain subspace performed poorly, even with the enhanced data, compared with frequency domain techniques. A frequency domain subspace method is shown to produce better results with the data processed using the Morlet time-frequency technique.