Deterministic learning and robot manipulator control

In this paper, based on a resent result on deterministic learning, we present an approach for robot manipulator control and learning. When a robot manipulator is controlled to track a periodic reference orbit, locally-accurate approximation of the closed-loop control system dynamics can be achieved in a local region along the periodic orbit. Moreover, the learned knowledge can be reused for the same or similar control tasks, so that the robot manipulator can be easily controlled with little effort. Simulation studies are included to illustrate the proposed approach.