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第37届中国控制会议

作者： Zhicheng Yang Lihui Geng Yan Yang Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical EngineeringTianjin University of Technology and Education

A frequency-domain identification method with higher computational efficiency is proposed in this paper. The minimum v-gap metric between the disturbing model and the estimation model is used as an objective function. By change the constraints way for the Nyquist winding condition, the computational efficiency is much higher than the way of previous pointto-point constraint. The proposed method is applicable to not only the errors-in-variables models but also the conventional output noise corrupted models. Parameter optimization of the estimation model employs the interior-point method, and thus the computational efficiency is greatly improved in comparison with the linear matrix inequalities based identification method. The length of experimental data is typically larger than the order of the estimation model, so the calculational efficiency is improved ***, a simulation experiment is given to demonstrate that the proposed identification method is computationally efficient.

关键词： v-gap metric Computational Efficiency Nyquist Winding Condition Interior-point Method

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L-two-optimal identification of errors-in-variables models:a frequency-domain approach

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控制理论与应用（英文版） 2011年第4期9卷 553-558页

作者： Lihui GENG Deyun XIAO Tao ZHANG Jingyan SONG Department of Automation Tsinghua University Beijing 100084 China

This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model （EIvM） identification. With normalized coprime factor model （NCFM） representations, L-two-optimal approximate models are derived from the framework of an EIvM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities （LMIs）. In terms of the optimized system model, the noise model （NM） can be readily obtained by right multiplication of an inner. Compared with other EIvM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiabiUty. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.

关键词： Errors-in-variables model Normalized coprime factor model v-gap metric Linear matrix inequalities

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