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Open AccessArticle

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China
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Energies 2018, 11(4), 778; https://doi.org/10.3390/en11040778
Received: 29 January 2018 / Revised: 22 March 2018 / Accepted: 26 March 2018 / Published: 28 March 2018
In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results. View Full-Text
Keywords: full-order observer; parameter identification; motor control full-order observer; parameter identification; motor control
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Yang, M.; Liu, Z.; Long, J.; Qu, W.; Xu, D. An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares. Energies 2018, 11, 778.

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