An Adaptive Sliding Mode Control for PMSM Servo Systems Beyond the Inertia Ratio Limit
The Permanent Magnet Synchronous Motor (PMSM) servo systems can only drive loads with relatively low inertia due to the limitation of the ratio between load inertia and rotor inertia (inertia ratio). Once the inertia ratio goes beyond the limit, the PMSM servo systems exhibit deviation. This article introduces an adaptive sliding mode control for the PMSM servo systems beyond the inertia ratio limit (ASMC-BIRL). First, a direct position control model was established for the PMSM to achieve dual-loop merging, effectively eliminating mutual interference between the loops during parameters adjustment. Second, a position controller based on second-order adaptive sliding mode is designed, and through dynamic parameter adjustment, it effectively suppresses the overshoot problem caused by excessive inertia ratio. Next, a method to suppress chattering while maintaining the fastest response speed and a method to further improve positional accuracy are proposed. Finally, dynamic control of position, speed and acceleration was realized by tracking the dynamic trajectory curve. Experiments show that ASMC-BIRL overcomes inertia ratio limitations. Compared to existing servo methods, the system tolerates load inertia hundreds of times greater. Moreover, compared with current braking-based PMSM position control methods, it achieves higher accuracy, smaller speed ripple and lower energy loss.
Authors
- Xitong Niu (ORCID: https://orcid.org/0009-0000-2589-6251)
- S Wang (ORCID: https://orcid.org/0009-0002-5660-2177)
- Wu Lu (ORCID: https://orcid.org/0000-0001-5237-5486)
- Zheming Wang (ORCID: https://orcid.org/0000-0002-3842-2300)
- Yongsheng Liu (ORCID: https://orcid.org/0000-0002-3389-7921)
- Yanyan Zhu
Institutions
- Shanghai University of Electric Power (CN)
Publication Details
- Journal
- IEEE Transactions on Power Electronics
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1109/tpel.2025.3580353
- Citations
- 1
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China