New neural network control technique for non-model based robot manipulator control

In this paper a new neural network (NN) control technique for non-model based PD control of robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique of Kawato et al. is that compensation of robot dynamics uncertainties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control algorithms are developed. One is comparable to that in the FEL technique and the another involves the Jacobian of the PD controlled robot dynamic system. Performances of both controllers with different trajectories and PD controller gains are examined and compared with that of the FEL controller. It is shown that the new control technique is superior.