Iterative learning in ballistic control

Abstract

In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The 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. To verify the effectiveness of iterative learning methods in ballistic control problems, the basketball shot process is formulated and studied. The simulation results show that the desired shooting force and angle can be acquired quickly through iterative learning. © 2007 IEEE.