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Observer-based event and self-triggered adaptive output feedback control of robotic manipulators 期刊论文
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2022, 页码: 32
作者:  Gao, Jie;  He, Wei;  Qiao, Hong
收藏  |  浏览/下载：165/0  |  提交时间：2022/11/14
first-order filter  impulsive dynamical system  model-based event-triggered control  neural network  nonlinear uncertainty  observer estimation  robotic manipulator  
Evolution and neural representation of mammalian cooperative behavior 期刊论文
CELL REPORTS, 2021, 卷号: 37, 期号: 7, 页码: 17
作者:  Jiang, Mengping;  Wang, Miaoyaoxin;  Shi, Qianqian;  Wei, Lei;  Lin, Yongqin;  Wu, Dingcheng;  Liu, Boyi;  Nie, Xiupeng;  Qiao, Hong;  Xu, Lin;  Yang, Tianming;  Wang, Zuoren
收藏  |  浏览/下载：189/0  |  提交时间：2021/12/28
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
收藏  |  浏览/下载：280/0  |  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)  
Computational modeling of Emotion-motivated Decisions for Continuous Control of Mobile Robots 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2020, 卷号: 13, 期号: 2020, 页码: 1-14
作者:  Huang, Xiao;  Wu, Wei;  Qiao, Hong
浏览  |  Adobe PDF(5970Kb)  |  收藏  |  浏览/下载：249/86  |  提交时间：2020/06/09
Brain-inspired Computing  Emotion-motivated Learning  Emotion-memory Interactions  Decision-making  Reinforcement Learning  
Connecting Model-Based and Model-Free Control With Emotion Modulation in Learning Systems 期刊论文
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 卷号: 10, 期号: 4, 页码: 1-15
作者:  Huang, Xiao;  Wu, Wei;  Qiao, Hong
浏览  |  Adobe PDF(1614Kb)  |  收藏  |  浏览/下载：258/86  |  提交时间：2020/06/09
Brain-inspired computing  decision-making  emotion modulation  emotion-cognition interactions  reinforcement learning