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
Series elastic actuators place an elastic element between the motor and the load, which attenuates external impacts but can make precise load position control difficult. Load-Port Control (LPC) transforms the position control input into the load-port path, which reduces the phase lag seen by load position feedback, using complementary-filter-based acceleration estimation. However, LPC depends on the nominal stiffness in both the input transformation and the model-based acceleration estimation, so a mismatch between actual and nominal stiffness alters the reference response and the closed-loop characteristic equation, degrading performance. This paper proposes Stiffness-Compensated Load-Port Control (SC-LPC), which estimates stiffness from the motor-side dynamics residual and applies the estimate to the LPC input transformation, the feedback path, and the model-based acceleration branch. The singularity in the stiffness estimate at small spring deflections is mitigated through regularized regression of filtered signals with a projection. Closed-loop stability is assessed in two stages: a small-gain condition guarantees stability under the time-varying stiffness estimate, and equilibrium-point linearization evaluates local stability including motor model mismatch. Simulink simulations show that, for stiffness mismatch ratios from 0.5 to 2.0 with a matched motor model, SC-LPC reduces the worst-case root-mean-square error by about 95.3% and the worst-case peak difference by about 92.6% compared with the conventional complementary filter-based LPC. These results indicate that SC-LPC suppresses the effect of stiffness mismatch on the load position response.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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