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Genetic Optimization of Excitation Signals for Nonlinear Dynamic System Identification

Two new methods for optimization of passive step-based excitation signals for system identification of nonlinear dynamic processes via a genetic algorithm are introduced an optimized Amplitude Pseudo Random Binary Signal (APRBSOpt) and a Genetic Optimized Time Amplitude Signal (GOATS). The investigated optimization objectives are the evenly excitation of all frequencies and the uniform data distribution of the space spanned by the system’s input and output. The results show that the GOATS optimized according to the uniform data distribution outperform the state-of-the-art excitation signals standard ARPBS (APRBSStd), Optimized Nonlinear Input Signal (OMNIPUS), Chirp and Multi-Sine in the achieved model quality on three artificially created Single-Input Single-Output (SISO) nonlinear dynamic processes. However, the APRBSOpt only exceeds the Chirp, Multi-Sine and APRBSStd in the achievable model quality. Additionally, the GOATS can be used for stiff systems, supplementing existing data and easy incorporation of constraints.