On the dynamics of a neural network for robot trajectory tracking

In this paper, the dynamic behavior of a three-layer feedforward neural network as a uncertainty compensator for robotic control is investigated. The investigation is conducted in the context of the robot trajectory tracking problem, where the neural network (with the error-backpropagation algorithm) is used as a uncertainty compensator in conjunction with the feedback linearization control (i.e. computed torque) and a PD control. Through computer simulation, it is verified that the dynamics of the neural network has a specific pattern when the learning rate is sufficiently small, and that such a specific pattern of weight variation in the neural network represents a sufficient condition for closed-loop system performance improvement.