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.
Iterative Learning in Ballistic Control: Formulation of Spatial Learning Processes for Endpoint Control
Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received November 23, 2010; final manuscript received May 8, 2012; published online November 7, 2012. Assoc. Editor: Warren E. Dixon.
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Xu, J., and Huang, D. (November 7, 2012). "Iterative Learning in Ballistic Control: Formulation of Spatial Learning Processes for Endpoint Control." ASME. J. Dyn. Sys., Meas., Control. March 2013; 135(2): 024501. https://doi.org/10.1115/1.4007236
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