Efficient use of short data records for FRF modeling by using fractional poles

Modeling systems based on measurements is a well established field of research and engineering practice. The techniques available to build and identify these models operate under the assumption that “sufficiently many” measurements are available. In most cases, the model quality improves when the number of measurements increases. Unfortunately, measurement time is expensive and in some applications it is even infeasible to increase the number of measurements. For these kinds of applications, classical modeling tools become untrustworthy and no alternatives are available. In this paper, we introduce fractional order differential equations instead of ordinary differential equations to model linear systems. The major advantage of the presented technique is that only a small number of parameters is needed to obtain a very flexible model. We propose an identification technique which replaces the ordinary differential equations by fractional order differential equations with a smaller number of parameters.