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

Ming Yang, Zirui Liu, Jiang Long, Wanying Qu and Dianguo Xu
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Ming Yang: Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China
Zirui Liu: Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China
Jiang Long: Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China
Wanying Qu: Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China
Dianguo Xu: Institute of Power Electronics and Electrical Drives, Harbin Institute of Technology, Harbin 150001, China

Energies, 2018, vol. 11, issue 4, 1-17

Abstract: 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.

Keywords: full-order observer; parameter identification; motor control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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