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학술지 Stability Analysis of Discrete-time Learning Control Systems with Interval Uncertainty
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저자
안효성, Kevin L. Moore, YangQuan Chen
발행일
200705
출처
Automatica, v.43 no.5, pp.892-902
ISSN
0005-1098
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.automatica.2006.11.020
협약과제
07MI1300, USN 기반 Ubiquitous Robotic Space 기술 개발, 유원필
초록
This paper presents a stability analysis of the iterative learning control (ILC) problem for discrete-time systems when the plant Markov parameters are subject to interval uncertainty. Using the so-called super-vector approach to ILC, vertex impulse response matrices are employed to develop sufficient conditions for both asymptotic stability and monotonic convergence of the ILC process. It is shown that the stability of such interval ILC systems can be determined by checking the stability of the system using only the vertex points of the interval Markov parameters. © 2007 Elsevier Ltd. All rights reserved.
KSP 제안 키워드
Asymptotic Stability, Control systems, Interval uncertainty, Iterative learning control, Markov parameters, Monotonic convergence, Stability analysis, Sufficient conditions, Vector approach, discrete-time systems, impulse response