An evaluation of the influence of Eigensystem Realization Algorithm settings on multiple input multiple output system identification

One characteristic of the Eigensystem Realization Algorithm method for system identification concerns about the difficulty of finding more appropriate parameters to run the algorithm. One of this work's purposes is to tackle the cumbersome task of achieving the ideal algorithm settings, providing additional knowledge about algorithm parameters' influence, and searching to improve results with quicker settings of the algorithm's parameters, especially for Multiple Input Multiple Output (MIMO) systems. The application of a Fit Rate indicator to evaluate the identified system arises as a novelty in the Eigensystem Realization Algorithm applications, aiming to assess the system identification performance and drive the algorithm to better adjustments. Another objective of this article regards the application of a Pseudo Random Binary Sequence as excitation signals, which has not been used until now with the Eigensystem Realization Algorithm, despite being successfully applied in the system identification process. The proposed approach is verified and analyzed with numerical simulations for a mass-spring-damper model of 5 degrees of freedom. The results reported in time response analysis and frequency response analysis allow us to realize the effect of settings accordingly for the system identification improvement. The results analysis was extended to simulate and compare the Pseudo Random Binary Sequence with Gaussian white noise excitation, and the system was also submitted to the presence of measurement noise.

Automated summary

This article explores some characteristics and applications of the Eigensystem Realization Algorithm in system identification, addressing the difficulty of parameter settings for the algorithm, proposing a new method for evaluating the identified system, and attempting to use a Pseudo Random Binary Sequence as an excitation signal for system identification. Through numerical simulations and result analysis, it is found that the settings of parameters play a crucial role in improving system identification.