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dc.contributor.authorSun, Heqingen_GB
dc.contributor.authorHou, Zhongshengen_GB
dc.contributor.authorLi, Dayouen_GB
dc.date.accessioned2013-08-12T08:16:09Z
dc.date.available2013-08-12T08:16:09Z
dc.date.issued2012
dc.identifier.citationSun, H., Hou, Z. and Li, D. (2012) 'A norm optimal iterative learning control based train trajectory tracking approach', Decision and Control (CDC), IEEE 51st Annual Conference on, pp.3966-3971en_GB
dc.identifier.doi10.1109/CDC.2012.6426487
dc.identifier.urihttp://hdl.handle.net/10547/297886
dc.description.abstractA norm optimal iterative learning control (NOILC) is proposed and applied in train trajectory tracking problem, and it then is extended to the cases with traction/braking constraint. Rigorous theoretical analysis has shown that the proposed approach can guarantee the asymptotic convergence of train speed and position to desired profiles as iteration number goes infinity. Simulation results further demonstrate the effectiveness of the proposed NOILC approach.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6426487en_GB
dc.subjectadaptive controlen_GB
dc.subjectiterative methodsen_GB
dc.subjectlearning systemsen_GB
dc.titleA norm optimal iterative learning control based train trajectory tracking approachen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractA norm optimal iterative learning control (NOILC) is proposed and applied in train trajectory tracking problem, and it then is extended to the cases with traction/braking constraint. Rigorous theoretical analysis has shown that the proposed approach can guarantee the asymptotic convergence of train speed and position to desired profiles as iteration number goes infinity. Simulation results further demonstrate the effectiveness of the proposed NOILC approach.


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