Please use this identifier to cite or link to this item:
|Title:||Iterative learning in ballistic control: Formulation of spatial learning processes for endpoint control||Authors:||Xu, J.-X.
|Issue Date:||2013||Citation:||Xu, 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||Abstract:||In 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.||Source Title:||Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME||URI:||http://scholarbank.nus.edu.sg/handle/10635/82590||ISSN:||00220434||DOI:||10.1115/1.4007236|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.