Research on Dynamic Identification of Servo Motor Load Inertia Based on the Error Gain Factor Model

Fang Xie, Fei Yu and Chaochen An
Additional contact information
Fang Xie: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Fei Yu: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Chaochen An: Engineering Research Center of Power Quality, Ministry of Education, Anhui University, Hefei 230601, China

Energies, 2021, vol. 14, issue 20, 1-17

Abstract: Aiming at solving the problems of slow motion and positioning deviation caused by the change of the moment of inertia of the servo motor due to different loads, an identification method for the moment of inertia on the basis of the error gain factor model is introduced into the controller, so that the moment of inertia can be obtained accurately and quickly under dynamic conditions. First, the electromagnetic and motion equation of the permanent magnet synchronous motor is built, and the logical relationship between the moment of inertia, torque, speed and other physical quantities is derived, so that the moment of inertia can be dynamically acquired. Second, in order to increase the identification accuracy, an adaptive function is introduced in the inertia identification model to replace the fixed parameters as an error gain factor (EGF). Third, the accuracy parameter is defined, and the identification algorithm on the basis of the EGF model is compared with the accuracy parameters of the existing identification method, which verifies that the improved algorithm has a better accuracy and speed. Finally, on the experimental platform, the working condition of unsteady speed is simulated. It is further verified that the proposed method has a high anti-interference capability.

Keywords: permanent magnet synchronous motor; moment of inertia; parameter identification; error gain factor (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: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/20/6664/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/20/6664/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:20:p:6664-:d:656512

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().