A robust neural network control of robot manipulator for industrial application

In this paper, we present two kinds of robust control schemes for robot system which has the parametric uncertainties. In order to compensate these uncertainties, we use the neural network control system that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The reliability of the control scheme is shown by computer simulations and experiment of robot manipulator with 8 axis.