Uncertainty Evaluation Under Dynamic Conditions Using Polynomial Chaos Theory

This paper addresses the evaluation of dynamic uncertainty in dynamic measurements via polynomial chaos theory (PCT). In particular, this PCT analysis can be used in support of the design of a dynamic compensator for a given dynamic uncertainty target. Assuming that the filter that implements the dynamic compensator is designed based on the dynamic model of the transducer, then, the filter is affected by the parametric uncertainty with which the model is known. Furthermore, the order of the filter itself affects the propagation of uncertainty due to noise and uncertainty of the coefficients. In particular, in this paper, the inverse model filter is assumed to be built as the combination of elemental differentiator FIRs, each realizing numerically the first derivative. This analysis therefore provides support in finding a tradeoff between filter size, thus computational effort and uncertainty.

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