An adaptive neural network controller for visual tracking of constrained robot manipulators

Diverse image-based tracking schemes of robot moving in free motion have been proposed, and experimentally validated, whose position and velocity image tracking errors converge to zero. However, visual servoing for constrained motion robot tasks has not been addressed so as to provide control schemes that guarantee simultaneous tracking of position, velocity and contact force trajectories for dynamic robot models. The main difficulty lies from the fact that camera information cannot be used to drive force trajectories. Recognizing this fact, in this paper a unique error manifold that includes position-velocity errors and force errors in orthogonal complements is proposed under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. This seemingly fusion of all tracking errors into a unique error variable allows to propose a new control system which guarantees local exponential tracking of all error trajectories. A neural network, driven by an orthogonalized second order sliding mode surface is derived to compensate approximately for nonlinear robot dynamics. Residual errors that arise because of the finite size of the neural network are finally eliminated via two sliding modes. Simulations results are presented and discussed.