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This paper presents a robust-control-oriented system identification method aiming to minimize the normalized coprime factor uncertainty of the performance-weighted system. The nominal model and the normalized coprime factor uncertainty bound are estimated employing a filter bank approach, which approximately calculates the chordal distance between the identified model and the true system in the frequency range of interest. An iterative LMI optimization is formulated to identify the model coefficients. The optimal stability margin is also calculated and compared to the identified nominal uncertainty bound to decide if re-identification is necessary.

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In this letter, we present a novel approach of valve stiction detection using wavelet technology. A new non-invasive method is developed with the closed-loop normal operating data. The redundant dyadic discrete wavelet transform is used to decompose the data at different resolution scales. Based on the Lipschitz regularity theory, wavelet coefficients analysis across scales is performed to detect the jumps in the controlled variables. Adaptive wavelet de-noising is then applied to the data. Features of the valve stiction patterns are extracted from the de-noised data and the valve stiction probability is calculated.

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