Modeling and Detection of Limit-Cycle Oscillations Using Adaptable Linear Models

A method for modeling the flutter response of a thin winged aircraft is presented. A hybrid physical-adaptive modeling framework is proposed to separate the autoregressive and moving average flutter components. Adaptive oscillators set at the structural free-vibration modal frequencies of the wing represent the structure (the autoregressive component). The moving average filters represent signal changes caused by aerodynamic forces encountered during flight. Connected in series, these modules form a hybrid model for wing flutter under changing flight conditions. The moving average component is trained to predict the signal produced by an accelerometer at the wing tip. The residual is segmented using an analysis of variance. The resulting family of linear moving average models provides a synthesis of the wing’s response over time. Our analysis shows that this modeling paradigm performs well with data taken at increasing Mach numbers in level flight. Network parameters are shown to correlate linearly with Mach. Network components can themselves be predicted as functions of Mach number, allowing the accelerometer signals to be predicted with a high degree of accuracy. The paradigm is further validated by using an adapted model with a separate set of data from a different but similar flight condition.