State-Space System Identification beyond the Nyquist Frequency with Collaborative Non-Uniform Sensing Data

In a multirate sampled-data system encompassing a continuous-time process and multiple output samplers with different sampling periods, we introduce an innovative approach that leverages non-uniform data collated through a coprime collaborative sensing mechanism. The ultimate aim is to identify the intricate dynamics governing the system. The predominant challenge – relating to the accurate identification and representation of the multirate system dynamics – is addressed by pioneering a lifted state-space model for the system. This model is achieved by building upon and extending the subspace system identification. Moving forth, using this elevated model as a foundational basis, we seamlessly extract the single-rate system through an eigenvalue decomposition process. The proposed methodology’s efficacy is empirically tested through demonstrative examples with multiple orders and varying coefficients.

Conference paper