文献类型：期刊文献

英文题名：On Generalized RMP Scheme for Redundant Robot Manipulators Aided With Dynamic Neural Networks and Nonconvex Bound Constraints

作者：Xie, Zhengtai[1];Jin, Long[1];Du, Xiujuan[2];Xiao, Xiuchun[3];Li, Hongxin[1];Li, Shuai[4]

机构：[1]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China;[2]Qinghai Normal Univ, Key Lab IoT Qinghai Prov, Xining 810008, Qinghai, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[4]Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China

年份：2019

卷号：15

期号：9

起止页码：5172

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：SCI-EXPANDED(收录号：WOS:000489584600031)、、WOS

基金：This work was supported in part by the National Natural Science Foundation of China (with number 61703189), in part by the Fund of Key Laboratory of IoT of Qinghai Province (2017-ZJ-Y21), in part by the International Science and Technology Cooperation Program of China (2017YFE0118900), in part by the Natural Science Foundation of Gansu Province, China, under Grant 18JR3RA264, in part by the Sichuan Science and Technology Program (19YYJC1656), in part by the Natural Science Foundation of Hunan Province (2017JJ3257), in part by the Key Lab of Digital Signal and Image Processing of Guangdong Province (2016GDDSIPL-02), in part by the Doctoral Initiating Project of Guangdong Ocean University (E13428), in part by the Innovation and Strength Project of Guangdong Ocean University (Q15090), and in part by the Research Foundation of Education Bureau of Hunan Province, China (17C1299). Paper no. TII-18-2758.

语种：英文

外文关键词：Dynamic neural network; gradient descent; kinematic control; manipulability; nonconvex constraint; repetitive motion planning (RMP); simulative results

外文摘要：In this paper, in order to analyze the existing repetitive motion planning (RMP) schemes for kinematic control of redundant robot manipulators, a generalized RMP scheme, which systematizes the existing RMP schemes, is presented. Then, the corresponding dynamic neural networks are derived, which leverage the gradient descent method with the velocity compensation with the feasibility proven theoretically. Given that the position errors of the end-effector should be tiny enough in the applications of redundant robot manipulators when executing a given task, especially for a precision instrument, the performance analyses on the control schemes are urgently desirable. In this paper, the upper bound of the position error on the existing RMP schemes is deduced theoretically and verified by computer simulations, with the relationship between the position error and the manipulability derived. In addition, dynamic neural networks are constructed to solve the generalized RMP schemes, with the joint velocity limits in RMP schemes extended to the nonconvex constraint. Finally, computer simulations based on different redundant robot manipulators and comparisons based on different controllers are conducted to verify the feasibility of the generalized RMP scheme and the proposed dynamic neural networks.