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|Title:||Ballistic learning control: Formulation, analysis and convergence|
|Authors:||Xu, J. |
Initial state learning
Iterative learning control
|Source:||Xu, J.,Huang, D.,Wang, W. (2013-08). Ballistic learning control: Formulation, analysis and convergence. Journal of Control Theory and Applications 11 (3) : 325-335. ScholarBank@NUS Repository. https://doi.org/10.1007/s11768-013-2092-0|
|Abstract:||In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control (ILC) theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into iterative learning. With the help of trial results, proper control and updating direction can be determined. Then, iterative learning can be applied to ballistic control problem. Several initial state learning algorithms are studied for initial speed control, force control, as well as combined speed and angle control. In the end, shooting angle learning in the basketball shot process is simulated to verify the effectiveness of iterative learning methods in ballistic control problems. © 2013 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.|
|Source Title:||Journal of Control Theory and Applications|
|Appears in Collections:||Staff Publications|
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