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The effectiveness of data-driven techniques significantly relies on the input signal used to generate the training data. Nevertheless, there is a notable gap in research when it comes to designing excitation signals for identifying nonlinear dynamic systems, likely because of the challenges involved. Based on current knowledge, it is crucial for excitation signals to effectively capture the nonlinearity across the entire operational area and to gather insights into the area-specific dynamic process characteristics. The Incremental Dynamic Space-Filling Design (IDS-FID) strategy designs excitation signals to achieve a space-filling distribution across the input space of a nonlinear approximator used in external dynamics modeling, gathering information throughout its operational area. Simultaneously, the approach enables for a heightened focus on either the systems steady-state or transient responses during information acquisition by altering the excitation signals dynamics, facilitating targeted insights into dynamic process characteristics., Comment: Will be published in 4th MECC (2024)