Adaptive neural networks-based visual servoing control for manipulator with visibility constraint and dead-zone input

This paper proposed an online image-based visual servoing (IBVS) controller for manipulator systems with dead-zone input. The adaptive neural networks (NNs) are used to approximate the unknown nonlinear dynamics. The Barrier Lyapunov Function (BLF) is constructed to overcome the visibility constraint problem, in which both the constant symmetric barriers and time-varying asymmetric barriers are considered. With the proposed control method, it is proved that all the signals in the closed-loop system are semi-globally uniformly bounded and the image error is remained in a bounded compact set. Finally, simulation examples are given to illustrate the effectiveness of the proposed control method.