Please use this identifier to cite or link to this item: https://doi.org/10.1115/1.4007236
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dc.titleIterative learning in ballistic control: Formulation of spatial learning processes for endpoint control
dc.contributor.authorXu, J.-X.
dc.contributor.authorHuang, D.
dc.date.accessioned2014-10-07T04:31:10Z
dc.date.available2014-10-07T04:31:10Z
dc.date.issued2013
dc.identifier.citationXu, J.-X., Huang, D. (2013). Iterative learning in ballistic control: Formulation of spatial learning processes for endpoint control. Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME 135 (2) : -. ScholarBank@NUS Repository. https://doi.org/10.1115/1.4007236
dc.identifier.issn00220434
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/82590
dc.description.abstractIn this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems, where the projectile experiences a constant gravitational force and a fluid drag force that is quadratic in speed. Three scenarios are considered in the spatial learning process, where the shooting speed, shooting angle, or their combination, are, respectively, the manipulated variables. The viewed endpoint displacement is the controlled variable. Under the framework of iterative learning control, ballistic learning convergence is derived in the presence of process uncertainties. In the end, an illustrative example is provided to verify the validity of the proposed ballistic learning control schemes. © 2013 by ASME.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1115/1.4007236
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1115/1.4007236
dc.description.sourcetitleJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
dc.description.volume135
dc.description.issue2
dc.description.page-
dc.description.codenJDSMA
dc.identifier.isiut000326088700022
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