Aeroservoelastic Uncertainty Model Identification From Flight Data

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

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.