The design of constrained, "plant-friendly" multisine input signals that optimize a geometric discrepancy criterion arising from Weyl's Theorem is examined. Such signals are meaningful for data-centric estimation methods, where uniform coverage of the output state-space is critical. The usefulness of this problem formulation is demonstrated by applying it to a linear example and to the nonlinear, highly interactive distillation column model developed by Weischedel and McAvoy. The optimization problem includes a search for both the Fourier coefficients and phases in the multisine signal, resulting in an uniformly distributed output signal displaying a desirable balance between high and low gain directions. The effectiveness of data resulting from a Weyl criterion-based signal for Model-on-Demand Model Predictive Control (a data-centric multivariable control algorithm) is demonstrated for the distillation column case study. The power of the proposed framework lies in its flexibility, allowing the user to incorporate both linear and nonlinear models for output prediction, time-domain constraints, and information and control-theoretic frequency domain requirements. This is an abstract of a paper presented at the AIChE Annual Meeting and Fall Showcase (Cincinnati, OH 10/30/2005-11/4/2005).