Iterative Excitation Signal Design for Nonlinear Dynamic Black-Box Models

Abstract A new method to generate excitation signals for the identification of nonlinear dynamic processes is introduced. The objective of the optimization is a uniform data point distribution in the input space of the nonlinear approximator. This optimization of the excitation signal is passive, thus the whole signal is optimized prior to the measurement of the process and no online adaptation is performed. The possibility to reuse already existing data sets is one of the key features of the proposed excitation signal optimization. The existing data sets are considered during the optimization, thus operating points with a high data point density are omitted and unexplored areas are filled with new data points. The advantages of the continued optimization are highlighted on artificial processes.