Iterative Learning in Ballistic Control: Formulation of Spatial Learning Processes for Endpoint Control

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.