Neural network-based nonlinear tracking control of kinematically redundant robot manipulators

In this paper, neural network-based nonlinear dynamical control of kinematically redundant robot manipulators is considered. The neural network-based controller achieves end-effector trajectory tracking as well as subtask tracking effectively. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. The whole system is shown to be stable in the sense of Lyapunov. Numerical simulation studies are carried out for a 3R planar robot manipulator to show the effectiveness of the control scheme.