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초록
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.
키워드
Position Estimation; Sliding Mode Observer; Adaptive gain; Permanent Magnet Stepping Motor; Permanent magnets; Stepping motors; Adaptive gain; Different structure; Finite-time convergence; Permanent magnet stepping motors; Position estimation; Sliding-mode observer; Super twisting algorithm; Super- twisting; Uncertainty; Unknown bounded perturbation; Sliding mode control
- 제목
- Position Estimation of Stepping Motor Using Adaptive Gain Super Twisting Algorithm Sliding Mode Observer
- 저자
- Son, Hyun Uk; Jeong, Yong Woo; Chung, Chung Choo
- 발행일
- 2021-12
- 유형
- Proceedings Paper
- 저널명
- 2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021)
- 권
- 2021-Octob
- 페이지
- 566 ~ 570