Abstract

Abstract A methodology to generate excitation signals that can be used for the identification of nonlinear dynamic systems is proposed. In contrast to traditional approaches which are based on specific signal types, the objective here is the homogeneous distribution of the data points in the input space of the model. A space-filling data distribution in the whole input space is a necessity for gathering information about the nonlinearities of the system and minimizes the risk of extrapolation. The methodology can be extended to multiple inputs with moderate increase in complexity which is a key feature for most real-world applications. The quality of the excitation signal is demonstrated with simulations and on a high pressure fuel supply system.

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