Nonconvex Noise-Tolerant Neural Model for Repetitive Motion of Omnidirectional Mobile Manipulators  

作　　者：Zhongbo Sun Shijun Tang Jiliang Zhang Junzhi Yu 

机构地区：[1]the Department of Control Engineering,Changchun University of Technology,Changchun 130012,China [2]IEEE [3]College of Information Science and Engineering,Northeastern University,Shenyang 110819,China [4]the State Key Laboratory for Turbulence and Complex Systems,Department of Advanced Manufacturing and Robotics,College of Engineering,Peking University,Beijing 100871,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第8期1766-1768,共3页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(61873304,62173048,62106023);the Key Science and Technology Projects of Jilin Province,China(20210201106GX);the Innovation and Entrepreneurship Talent funding Project of Jilin Province(2022QN04);the Changchun Science and Technology Project(21ZY41);Beijing Natural Science Foundation(2022MQ05)。

摘　　要：Dear Editor,Quadratic programming problems(QPs)receive a lot of attention in various fields of science computing and engineering applications,such as manipulator control[1].Recursive neural network(RNN)is considered to be a powerful QPs solver due to its parallel processing capability and feasibility of hardware implementation[2].

关 键 词：directional PROGRAMMING HARDWARE 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]