Trajectory tracking of robot manipulator using Gaussian networks

This paper presents a neural network based control scheme for robot tracking applications. Gaussian networks are used to model both the forward and the inverse dynamics of a robot arm. The feedforward torque is actuated by the output of inverse dynamic mapping while the feedback control law is derived using the linear perturbation model of the identified forward dynamics along the desired trajectory. The gradient based learning algorithm is used and the effectiveness of the proposed scheme is highlighted through simulation studies.