Optimized Excitation Signal Tailored to Pertinent Dynamic Process Characteristics

TL;DR

This paper introduces a novel excitation signal design strategy, IDS-FID, tailored to capture nonlinear system dynamics comprehensively and focus on specific dynamic responses, enhancing data-driven system identification.

Contribution

The paper presents the IDS-FID strategy, which optimally designs excitation signals for nonlinear systems, enabling targeted and comprehensive dynamic process characterization.

Findings

Effective space-filling excitation signals are generated across the operational input space.

The approach allows focusing on steady-state or transient responses as needed.

Enhanced system identification accuracy demonstrated through tailored excitation signals.

Abstract

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.