인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
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.
본문·목차
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