Robust Visual Servoing of Robot Manipulators with Neuro Compensation

This paper considers the problem of visual servoing of planar robot manipulators in the presence of uncertainty associated with robot dynamics, camera system and Jacobian matrix. By using radial basis function neural networks, these uncertainties can be compensated effectively. Two kinds of robust visual servoing are proposed, one is for image uncertainties, another is for Jacobian matrix uncertainty. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper.