Accelerated Norm-Optimal Iterative Learning Control
This paper proposes a novel technique for accelerating the convergence of the previously published Norm-Optimal Iterative Learning Control (NOILC) methodology. The basis of the results is a formal proof of an observation made by the first author, namely that the NOILC algorithm is equivalent to a successive projection algorithm between linear varieties in a suitable product Hilbert space. This leads to two proposed accelerated algorithms together with well-defined convergence properties. The results show that the proposed accelerated algorithms are capable of ensuring monotonic error norm reductions and can outperform NOILC by more rapid reductions in error norm from iteration to iteration. In particular, examples indicate that the approach can improve the performance of NOILC for the problematic case of non-minimum phase systems. Realization of the algorithms is discussed and numerical simulations are provided for comparative purposes and to demonstrate the numerical performance and effectiveness of the proposed methods.
David H Owens Bing Chu
Department of Automatic Control and Systems Engineering The University of She±eld, Mappin Street, She±eld S1 3JD, UK
国际会议
2008 Sino-European Workshop on Intelligent Robots and Systems(SEIROS08)(第一届中欧智能系统及机器人国际学术研讨会)
重庆
英文
1-8
2008-12-11（万方平台首次上网日期，不代表论文的发表时间）