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RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 2, 页码: 615-628
作者:  Xie, Zhengtai;  Jin, Long;  Luo, Xin;  Sun, Zhongbo;  Liu, Mei
收藏  |  浏览/下载:49/0  |  提交时间:2022/08/22
Manipulators  Kinematics  Recurrent neural networks  Task analysis  Redundancy  Error elimination  gradient descent method  orthogonal projection method  recurrent neural network (RNN)  redundant manipulators  repetitive motion generation (RMG)  
Perturbed Manipulability Optimization in a Distributed Network of Redundant Robots 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 卷号: 68, 期号: 8, 页码: 7209-7220
作者:  Jin, Long;  Zhang, Jiazheng;  Luo, Xin;  Liu, Mei;  Li, Shuai;  Xiao, Lin;  Yang, Zihao
收藏  |  浏览/下载:167/0  |  提交时间:2021/08/20
Manipulators  Optimization  Task analysis  Kinematics  Recurrent neural networks  Distributed control  generalized recurrent neural network (GRNN)  manipulability optimization (MO)  redundancy resolution  kinematic control  
Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13
作者:  Qi, Yimeng;  Jin, Long;  Luo, Xin;  Shi, Yang;  Liu, Mei
收藏  |  浏览/下载:44/0  |  提交时间:2022/08/22
Task analysis  Robustness  Biological neural networks  Resource management  Dynamic scheduling  Convergence  Recurrent neural networks  Consensus filter  k-winner-take-all (k-WTA)  multiagent coordination  theoretical analysis  
Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 12
作者:  Qi, Yimeng;  Jin, Long;  Luo, Xin;  Zhou, MengChu
收藏  |  浏览/下载:47/0  |  提交时间:2022/08/22
Computational modeling  Mathematical model  Neural networks  Control theory  Analytical models  Real-time systems  Numerical models  Control-theoretical techniques  perturbed nonstationary quadratic program (QP)  recurrent neural dynamics  robustness theoretical analysis