A robust model-based test planning procedure

A wide variety of model-based optimal test design methodologies have been developed in the past decade using deterministic approaches. This means that the test planning is based on a single-nominal model and an optimal design is obtained for precisely this model. Needless to say, the deterministic approach can lead to an ineffective distribution of sensors and poorly defined excitation points due to the presence of epistemic modelling errors. In this article, a robust-satisficing design approach to test planning is proposed based on info-gap decision theory. This methodology provides a decision-making tool for better understanding the trade-off between an optimal test design with no robustness to modelling uncertainties and a sub-optimal design which satisfies a less demanding level of performance while remaining maximally robust with respect to a given horizon of info-gap model uncertainty. The proposed strategy is illustrated using an aerospace application under base excitation conditions.