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Markov Parameter Identification via Chebyshev Approximation by Li, Jiayun; Mo, Yilin is a scholarly article available to read on EtoBox.

What is Markov Parameter Identification via Chebyshev Approximation about?

This paper proposes an identification algorithm for Single Input Single Output (SISO) Linear Time-Invariant (LTI) systems. In the noise-free setting, where the first $T$ Markov parameters can be precisely estimated, all Markov parameters can be inferred by the linear combination of the known $T$ Markov parameters, of which the coefficients are obtained by solving the uniform polynomial approximation problem, and the upper bound of the asymptotic identification bias is provided. For the finite-time identification scenario, we cast the system identification problem with noisy Markov parameters into a regularized uniform approximation problem. Numerical results demonstrate that the proposed algorithm outperforms the conventional Ho-Kalman Algorithm for the finite-time identification scenario while the asymptotic bias remains negligible.

Author

Li, Jiayun; Mo, Yilin

Published

2023

Language

EN