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학술대회 LMI Approach to Iterative Learning Control Design
Cited 7 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
안효성, Kevin L. Moore, YangQuan Chen
발행일
200607
출처
Proc. of the IEEE 2006 Mountain Workshop on Adaptive and Learning Systems, pp.72-77
DOI
https://dx.doi.org/10.1109/SMCALS.2006.250694
협약과제
06MI1300, USN 기반 Ubiquitous Robotic Space 기술 개발, 유원필
초록
This paper uses linear matrix inequalities to design iterative learning controller gains. Comparisons are made between Arimoto-style gains, causal gains, and non-causal gains, using the supervector approach. The results show that linear time-varying gains have better performance than linear time invariant gains and non-causal terms make the system more stable in the sense of monotonic convergence. © 2006 IEEE.
KSP 제안 키워드
Control design, LMI approach, Linear Matrix Inequalities(LMIs), Linear Time Invariant(LTI), Linear time-varying, Matrix inequality, Monotonic convergence, iterative learning controller, non-causal