Tracking control of robot manipulator robust output based on neural network

For the tracking control of robust output of a class of robot manipulators with unknown parameters, an adaptive robust control based on the neural network for robot manipulator was developed by applying the input/output feedback linearization method and the Lyapunov stability theory. The unknown non-linear functions were modeled with a three-layer forward neural network. The network weights were updated by the Lyapunov stability in real time, and the stability of the closed-loop systems was then guaranteed by this method. Moreover, the proposed control guaranteed that the output tracking error and the states of the established closed-loop system were uniformly bounded. The up-boundary of the uncertainties was not known in advance. The tracking control of the two DOF rigid robot manipulators with motive joint was used as an example for simulation to show that the proposed method was effective.