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Title: A Loewner Matrix Based Convex Optimization Approach to Finding Low Rank Mixed Time/Frequency Domain Interpolants
We consider the problem of finding the lowest order stable rational transfer function that interpolates a set of given noisy time and frequency domain data points. Our main result shows that exploiting results from rational interpolation theory allows for recasting this problem as minimizing the rank of a matrix constructed from the frequency domain data (the Loewner matrix) along with the Hankel matrix of time domain data, subject to a semidefinite constraint that enforces stability and consistency between the time and frequency domain data. These results are applied to a practical problem: identifying a system from noisy measurements of its time and frequency responses. The proposed method is able to obtain stable low order models using substantially smaller matrices than those reported earlier and consequently in a fraction of the computation time.
Authors:
;
Award ID(s):
1808381 1646121 1638234
Publication Date:
NSF-PAR ID:
10186506
Journal Name:
2020 American Control Conference
Page Range or eLocation-ID:
5169 to 5174
Sponsoring Org:
National Science Foundation
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