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

作者：Zhang, Yu; Hua, Changchun^*; Li, Yafeng; Guan, Xinping

来源：Neurocomputing, 2019, 332: 44-55.

DOI：10.1016/j.neucom.2018.11.058

• Adaptive control
• Visual servoing
• Barrier Lyapunov function
• Neural networks
• Dead-zone input
• Visibility constraints

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 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.

• 出版日期2019-3-7
• 单位燕山大学; 中国科学院电工研究所; 上海交通大学