Abstract
The Unscented Transformation (UT) is used in this paper to provide a systematic procedure to perform identified model parameter uncertainty quantification. The statistics of signal space uncertainties (input output I/O experimental data) are first mapped into the I/O model space using the Observer Kalman/Filter Identification (OKID) theory. The statistics of the state space model parameters are then computed by an application of the unscented transform on the statistics of the system Markov parameters. Numerical simulations and comparisons with Monte-Carlo error statistics demonstrate the efficacy of UT for model parameter uncertainty calculations presented in this paper. The algorithm is applied to quantify uncertainties associated with the pitch dynamics model of a 2 degree of freedom helicopter model. It is also applied to quantify the error statistics associated with the dynamics of a flexible beam.
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