This paper focuses on the problem of robust experiment design, i.e., how to design an input signal which gives relatively good estimation performance over a large number of systems and model structures. Specifically, we formulate the robust experiment design problem utilizing fundamental limitations on the variance of estimated parametric models as constraints. Using this formulation we design an input signal for situations where only diffuse a priori information is known about the system. Furthermore, we present a robust version of the unprejudiced optimal input design problem. To achieve this, we first develop a closed form solution for the input spectrum which minimizes the maximum weighted integral of the variance of the frequency response estimate over all model structures.