A bias-correction method for closed-loop identification of Linear Parameter-Varying systems

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Abstract

Due to safety constraints and unstable open-loop dynamics, system identification of many real-world processes often requiresgathering data from closed-loop experiments. In this paper, we present a bias-correction scheme for closed-loop identification of Linear Parameter-Varying Input–Output (LPV-IO) models, which aims at correcting the bias caused by the correlation between the input signal exciting the process and output noise. The proposed identification algorithm provides a consistent estimate of the open-loop model parameters when both the output signal and the scheduling variable are corrupted by measurement noise. The effectiveness of the proposed methodology is tested in two simulation case studies.

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Mejari, M., Piga, D., & Bemporad, A. (2018). A bias-correction method for closed-loop identification of Linear Parameter-Varying systems. Automatica, 87, 128–141. https://doi.org/10.1016/j.automatica.2017.09.014

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