视觉伺服
计算机科学
稳健性(进化)
运动学
控制理论(社会学)
人工神经网络
人工智能
控制器(灌溉)
方案(数学)
机器人
循环神经网络
计算机视觉
控制(管理)
数学
数学分析
生物化学
化学
物理
经典力学
生物
农学
基因
作者
Ning Tan,Peng Yu,Wenka Zheng
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2024-04-01
卷期号:54 (4): 2446-2459
被引量:2
标识
DOI:10.1109/tcyb.2022.3227470
摘要
Neural networks have been widely investigated for the control of robot manipulators and recurrent neural network (RNN) is accepted as a powerful tool for visual servoing. Different from existing control schemes for robot-camera systems, this article proposes a novel image-based visual servoing (IBVS) control scheme for both the regulation and tracking control of robot manipulators in the framework of a special class of RNN, termed zeroing neural network (ZNN), which does not require prior knowledge about camera configuration and kinematic model parameters. The proposed control scheme is composed of a data-driven mapping estimator and a controller, both of which are designed based on ZNN. To facilitate the deployment of the proposed IBVS control scheme, a discrete-time version of the proposed control scheme is developed. Theoretical analysis for the proposed method is presented in terms of convergence, stability, and robustness. In addition, simulations and experiments are carried out based on different types of robot-camera systems to verify the efficacy and portability of the proposed control scheme for solving regulation and trajectory IBVS problems. Moreover, comparative studies are performed to reveal the merits of the proposed control scheme.
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