Position Estimation of Stepping Motor Using Adaptive Gain Super Twisting Algorithm Sliding Mode Observer
- Authors
- Son, Hyun Uk; Jeong, Yong Woo; Chung, Chung Choo
- Issue Date
- Dec-2021
- Publisher
- IEEE
- Keywords
- Position Estimation; Sliding Mode Observer; Adaptive gain; Permanent Magnet Stepping Motor
- Citation
- 2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021), v.2021-Octob, pp.566 - 570
- Indexed
- SCOPUS
- Journal Title
- 2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021)
- Volume
- 2021-Octob
- Start Page
- 566
- End Page
- 570
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140053
- DOI
- 10.23919/ICCAS52745.2021.9649788
- ISSN
- 2093-7121
- Abstract
- This paper presents an Adaptive Gain Super Twisting Sliding Mode Observer (AGSTA-SMO) for a permanent magnet stepping motor as position. Since the proposed algorithm has a different structure with the Super Twisting Algorithm Sliding Mode Observer (STA-SMO), the AGSTA-SMO ensures a global, finite-time convergence even with the unknown, bounded perturbations/uncertainties. With the experimental validation, we show that the position estimation performance of AGSTA-SMO outperforms comparing to the position estimation result of STA-SMO.
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