Adaptive Robust Visual Servoing/Force Control for Robot Manipulator With Dead-Zone Input

In this paper, we investigate the adaptive robust visual servoing/force control problem for the uncertain robot manipulator. The robot is considered to work with an unknown constraint surface and the unknown depth of feature point in image plane is assumed to be time-varying. The unknown constraint surface is linearly parameterized, and new adaptive laws are designed to estimate the unknown parameters online. In image based visual servoing control, the unknown time-varying depth plays a special role as it appears nonlinearly in the overall Jacobian matrix and cannot be adapted together with other uncertain kinematic parameters. To handle this problem, a compensatory depth-independent interaction matrix framework and the corresponding adaptive laws are proposed to compensate for the depth in the closed-loop dynamics. Moreover, the dead-zone input constraint is considered and handled by designing the robust compensatory terms and adaptive laws. With the proposed control scheme, it is proved that the image position and force tracking errors converge to zero asymptotically. Finally, numerical simulation and experimental results illustrate the effectiveness of the proposed approach.