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Motion Learning and Rapid Generalization for Musculoskeletal Systems Based on Recurrent Neural Network Modulated by Initial States 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 卷号: 14, 期号: 4, 页码: 1691-1704
作者:  Wang, Xiaona;  Chen, Jiahao;  Qiao, Hong
Adobe PDF(2080Kb)  |  收藏  |  浏览/下载:148/17  |  提交时间:2023/03/20
Muscles  Recurrent neural networks  Mathematical models  Bio-inspired control  Robot kinematics  Tendons  Musculoskeletal system  Biologically inspired control  motor cortex  movement preparation  musculoskeletal system  recurrent neural network (RNN)  
Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 16
作者:  Zhong, Shanlin;  Zhou, Junjie;  Qiao, Hong
收藏  |  浏览/下载:247/0  |  提交时间:2022/01/27
Robots  Modulation  Robot kinematics  Neurons  Brain modeling  Recurrent neural networks  Encoding  Biologically inspired control  gain modulation  motor primitives  musculoskeletal robot  recurrent neural network (RNN)  
Adaptive-Neural-Network-Based Trajectory Tracking Control for a Nonholonomic Wheeled Mobile Robot With Velocity Constraints 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 卷号: 68, 期号: 6, 页码: 5057-5067
作者:  Chen, Ziyu;  Liu, Yang;  He, Wei;  Qiao, Hong;  Ji, Haibo
Adobe PDF(1673Kb)  |  收藏  |  浏览/下载:325/12  |  提交时间:2021/04/06
Mobile robots  Lyapunov methods  Adaptive systems  Artificial neural networks  Automation  Adaptive neural networks  barrier Lyapunov function (BLF)  velocity constraint  wheeled mobile robot (WMR)  
Reducing Redundancy of Musculoskeletal Robot With Convex Hull Vertexes Selection 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020, 卷号: 12, 期号: 3, 页码: 601-617
作者:  Zhong, Shanlin;  Chen, Jiahao;  Niu, Xingyu;  Fu, Hang;  Qiao, Hong
收藏  |  浏览/下载:231/0  |  提交时间:2021/01/07
Muscles  Robots  Mathematical model  Hardware  Redundancy  Numerical models  Convex hull vertexes  human-like robot  motion experiment  musculoskeletal system  reduce redundancy