Discrete-time tracking control of robotic manipulators based on dynamic inversion using dynamic neural networks

Abstract

A stable discrete time tracking control approach based on dynamic inversion using dynamic neural networks (DNNs) is developed in the paper for robotic manipulators with unknown dynamics nonlinearities. Two novel design technologies-dynamic inversion constructed by DNNs and the NN variable structure control-are used for adaptive tracking controller design. The robot control law is composed of the dynamic inversion of the DNN system, adaptive compensation and the NN variable structure control (VSC) components. The developed control scheme guarantees the global stability and tracking error convergence of the NN control system. Finally, the control performance of the proposed control approach is illustrated through the comparison studies with robot tracking control approach using static NNs.