Online Identification Method for Mechanical Parameters of Dual-Inertia Servo System

Bo Wang, Runze Ji, Chengpeng Zhou, Kai Liu (), Wei Hua and Hairong Ye
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Bo Wang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Runze Ji: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Chengpeng Zhou: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Kai Liu: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Wei Hua: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Hairong Ye: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2024, vol. 18, issue 1, 1-18

Abstract: Rotary table servo systems are widely used in industrial manufacturing. In order to satisfy the demands of low-speed and high-torque applications, rotary table servo systems are typically applied with a reduction gear and gearbox, causing transmission system limit loop oscillation and reducing the system’s transmission accuracy. Accordingly, the single-axis servo rotary table is taken as the object of study, with the objective of enhancing the positioning precision of the load side. The identification of the mechanical parameters of the dual-inertia servo system is thus undertaken. A simplified mathematical model of the dual-inertia system is constructed, the principle of mechanical parameter identification of the dual-inertia system is elucidated, an online identification algorithm based on the forgetting factor recursive least square (FFRLS) is investigated, and factors affecting the identification accuracy are analyzed. The efficacy of the recognition algorithm is validated through simulations and experimentation. The experiments on the DSP 28,335 platform demonstrate that the dual-inertia system mechanical parameter recognition algorithm is capable of identifying rotor inertia, load inertia, and shaft stiffness online simultaneously. The recognition error is less than 10%, the recognition accuracy is high, and the algorithm exhibits a certain degree of robustness.

Keywords: dual-inertia elastic system; mechanical parameter identification; forgetting factor recursive least square method (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: 2024
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