Robust estimation from in-flight measurements must account for structured uncertainty, including nonlinear phenomena, to distinguish linear from nonlinear dynamics for uncertain model definition. The ability to identify and generate models describing nonlinear phenomena such as buzz and limit cycle oscillations is extremely important to Dryden and the flight test community for stabilty estimation. Nonlinear phenomena cannot be effectively suppressed without accurate dynamic models generated by such methods. Confidence bounds are desired in the identification scheme to guarantee system stability within a flight regime. Unknown system parameters identified from these data should be consistent with the uncertainty and disturbance models for an explicit measure of stability at any flight condition.
Wavelets provide systematic tools for signal processing of nonlinear and time-varying systems.
Research is under way to investigate methods of aeroelastic modeling with uncertainty descriptions consistent with the data. Dryden is studying linear and nonlinear identification algorithms and methodologies for in-flight aeroelastic robust stability determination. Use of advanced data processing procedures, such as wavelet decompositions and other multiresolution methods, are being encouraged for more informative data acquisition. Intelligent data processing and analysis routines are required to accomplish the goal of accurate stability determination during flight testing. This integrated research effort requires innovative data processing, modeling, identification, and robust system theory applications.