Learning Open-Loop Terminal Control

We apply two basic ideas of intelligent control: learning and approximation (generalization) of learned experience to a terminal control problem for a MLTI plant. We learn and store a set of feedforward programs for different problem parameters. With help of scattered data approximation we obtain the program for other parameter values. As an example, we consider learning of point-to-point motion control for a two-link flexible-joint manipulator.