Spike-Free Adaptive Sliding Mode Control: Application to Permanent Magnet Synchronous Motors
A new methodology for adaptive sliding mode control (ASMC) has been widely used to improve the control performance in various systems. This method exhibits several advantages, including low sliding mode control (SMC) chattering, no knowledge of the system disturbance bound, and no overestimation of the control gain. Despite its advantages, this method can be hampered by the spike phenomenon, slow control gain convergence, and difficulty in achieving optimal performance under varying disturbances. Consequently, this article proposes a spike-free ASMC method with a disturbance observer (DOB) to address these problems. Previous ASMC methods have been analyzed via simulations to verify the aforementioned problems. This analysis highlights the need for disturbance compensation and improvements in the SMC gain adaptation law. Therefore, a DOB is designed to mitigate the spike phenomenon by compensating for disturbances. Subsequently, an SMC gain adaptation law based on disturbance error estimation is designed to eliminate the spike phenomenon completely. The proposed adaptation law makes the SMC gain to converge to a slightly higher value than the disturbance estimation error. Consequently, the proposed method not only eliminates the spike phenomenon, but also ensures optimal performance under varying disturbances. The performance of the proposed method is experimen tally validated through a comparative study.
Authors
- Sesun You (ORCID: https://orcid.org/0000-0002-5446-9555)
- Kwankyun Byeon
- Jaeyun Yim (ORCID: https://orcid.org/0009-0004-8118-4215)
- Yonghao Gui (ORCID: https://orcid.org/0000-0002-5043-5534)
- Wonhee Kim (ORCID: https://orcid.org/0000-0001-9893-6381)
- Youngwoo Lee (ORCID: https://orcid.org/0000-0001-6432-3528)
Institutions
- Oak Ridge National Laboratory (US)
- National Transportation Research Center (US)
- Hanwha Solutions (South Korea) (KR)
- Hanyang University (KR)
- Keimyung University (KR)
- Chung-Ang University (KR)
Publication Details
- Journal
- IEEE Transactions on Industrial Electronics
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1109/tie.2025.3574392
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00