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This paper investigates the modeling of parameter-varying nonlinear aeroelastic systems using Volterra theory. With this approach, the system dynamics are expressed in terms of a set of Volterra kernels. It is well known that the parameters of aeroelastic systems, such as natural frequencies and damping ratios, change with flight condition. Therefore, a system is characterized by a different set of Volterra kernels at each flight condition. These kernels must be extracted from the flight data at each flight condition of interest: a relatively costly procedure. This paper presents a kernel extrapolation method designed to model first- and second-order kernels as functions of a single varying parameter, such as altitude or Mach number. First, using a multiwavelet-based algorithm, a set of Volterra kernels is identified from the input/output data at a number of flight conditions. In this manner, each kernel is represented in terms of a set of multiwavelet basis functions. An extrapolation model is then obtained by using a polynomial curve-fit to express the magnitude of each basis coefficient as a function of the varying parameter. The performance of this kernel extrapolation method is studied in detail for a simulated nonlinear aeroelastic system, demonstrating the validity of the approach. Then, the algorithm is applied to flight data from the F/A-18 Active Aeroelastic Wing. This example illustrates some of the difficulties associated with extrapolating kernels from noisy flight data.

A general nonlinear feedback framework to compute robust match-point solutions for aeroelastic/aeroservoelastic systems including uncertain and nonlinear operators is presented. Augmentation of the current match-point solution approach is sought using the [micro] method with nonlinear memoryless operators. An aeroelastic/aeroservoelastic model is built by proper interconnection of several linear fractional transformations, so that aerodynamic and structural nonlinear effects can be incorporated into a robust analysis setup. The describing function technique is used to determine the stability boundary of the nonlinear feedback system, so that limit-cycle oscillations (LCO) can be predicted and the boundary stability behavior characterized. The resulting quasilinear model allows the computation of robust stability margins that reflects both flutter and LCO instabilities. A typical section model with control surface freeplay and uncertain structural stiffness parameters is used to demonstrate the proposed aeroelastic/aeroservoelastic modeling framework. In general, good agreement was obtained with published numerical and experimental wind-tunnel test results. Comments are provided concerning possible limitations of the proposed modeling methodology.

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and "weak" fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced-order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design. [ABSTRACT FROM AUTHOR]