Neural network based hybrid force/position control for robot manipulators

This paper presents a neural network based adaptive control scheme for hybrid force/position control for rigid robot manipulators. Firstly the robot dynamics is decomposed into force, position and redundant joint subspaces. Based on this decomposition, a neural network based controller is proposed that achieves the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment as well as regulate robot tip position in cartesian space. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. Finally numerical simulation studies are carried out for a two link rigid robot manipulator.