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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter20200220.pdf (1.11 MB)

Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

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posted on 11.03.2020, 10:25 by Tai-shan Lou, Dong-xuan Han, Xiao-liang Yang, Su-xia Jiang
To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

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Email Address of Submitting Author

tayzan@sina.com

ORCID of Submitting Author

0000-0002-0624-6912

Submitting Author's Institution

Zhengzhou University of Light Industry

Submitting Author's Country

China

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