지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 1,321 - 1,328 (8page)
- DOI
- 10.5302/J.ICROS.2024.24.0217
이용수
초록· 키워드
오류제보하기
Integrating iterative learning control (ILC) with feedback control systems progressively enhances tracking performance by learning from data, such as control inputs and tracked errors accumulated over multiple trials. Despite the integration of ILC systems with existing feedback control systems, learning controllers have often been designed without effectively leveraging the information used in feedback controller design. Furthermore, the improper utilization of this information can degrade the performance of ILC systems. In this study, the ILC system comprises two learning filters: a learning filter and a robustness filter. The learning filter is directly derived from the inverse of the nominal feedback control system, while the robustness filter is a low-pass filter that ensures robust convergence under uncertainty. To design learning controllers, uncertainty is isolated using linear fractional transformation (LFT) within a transfer function based on established convergence conditions. A robust convergence condition in the -norm sense is formulated, and it is represented by the robustness filter, uncertainty weighting function, feedback controller, and nominal plant. Based on the derived convergence condition, criteria for the straightforward design of learning controllers were presented. The performance weighting function employed in the design of the feedback control system was excluded from the design of the ILC system, thereby ensuring unobstructed enhancement of the learning performance. Finally, simulation studies were conducted to demonstrate the feasibility of the proposed method.
목차
Abstract
I. 서론
II. 문제 설정
III. 수렴 조건과 강인 필터의 설계
IV. 시뮬레이션 연구
V. 결론 및 추후 과제
REFERENCES
참고문헌 (13)
참고문헌 신청
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D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control: A learning-based method for high-performance tracking control,” IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006.
T.-Y. Doh, J.-H. Moon, K. B. Jin, and M. J. Chung, “Robust ILC with current feedback for uncertain linear systems,” International Journal of Systems Science, vol. 30, no. 1, pp. 39-47, 1999.
Q. Hu, J.-X. Xu, and T. H. Lee, “Iterative learning control design for Smith predictor,” Systems and Control Letters, vol. 44, pp. 201-210, 2001.
K. L. Moore, “Iterative learning control: An expository overview,” Applied and Computational Control, Signals, and Circuits, vol. 1, pp. 512-214, 1999.
D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control: A learning-based method for high-performance tracking control,” IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006.
T.-Y. Doh, J.-H. Moon, K. B. Jin, and M. J. Chung, “Robust ILC with current feedback for uncertain linear systems,” International Journal of Systems Science, vol. 30, no. 1, pp. 39-47, 1999.
Q. Hu, J.-X. Xu, and T. H. Lee, “Iterative learning control design for Smith predictor,” Systems and Control Letters, vol. 44, pp. 201-210, 2001.
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