A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models  

作　　者：Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni 

机构地区：[1]IEEE [2]the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing,Ministry of Education,Sun Yat-sen University,Guangzhou 510006,China [3]the School of Automation,Guangdong Polytechnic Normal University,Guangzhou 510665,China [4]the State Key Laboratory of Robotics and Systems,Harbin Institute of Technology(HIT),Harbin 150080,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第10期1778-1791,共14页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62173352,62103112);the Guangdong Basic and Applied Basic Research Foundation(2021A1515012314);the Open Project of Shenzhen Institute of Artificial Intelligence and Robotics for Society(AC01202005006);the Key-Area Research and Development Program of Guangzhou(202007030004)。

摘　　要：Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关 键 词：Dual zeroing neural networks(ZNN) finite-time convergence MODEL-FREE robot control robustness analysis 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]