CASIA OpenIR

浏览/检索结果: 共14条,第1-10条 帮助

限定条件                
已选(0)清除 条数/页:   排序方式:
Removing Feasibility Conditions on Adaptive Neural Tracking Control of Nonlinear Time-Delay Systems With Time-Varying Powers, Input, and Full-State Constraints 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 卷号: 52, 期号: 4, 页码: 2553-2564
作者:  Guo, Chao;  Xie, Xue-Jun;  Hou, Zeng-Guang
收藏  |  浏览/下载:379/0  |  提交时间:2022/06/10
Nonlinear systems  Time-varying systems  Control design  Adaptive systems  Delay effects  Artificial neural networks  Automation  Feasibility conditions  input and full-state constraints  neural networks (NNs)  nonlinear systems  time-varying powers  
面向开放场景行人重识别的特征表示研究 学位论文
, 中科院自动化研究所: 中科院自动化研究所, 2021
作者:  王贯安
Adobe PDF(21494Kb)  |  收藏  |  浏览/下载:211/14  |  提交时间:2022/06/06
开放场景,行人重识别,机器视觉,深度学习  
Finite-Time Stability Control of Uncertain Nonlinear Systems With Self-Limiting Control Terms 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 6
作者:  Zhu, Jiaming;  Yang, Yuequan;  Zhang, Tianping;  Cao, Zhiqiang
收藏  |  浏览/下载:233/0  |  提交时间:2022/06/06
Artificial neural networks  Nonlinear systems  Control systems  Adaptive systems  Backstepping  Biological neural networks  Trajectory  Backstepping  dynamic surface control (DSC)  finite-time tracking  neural network (NN)  self-limiting term  
Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13
作者:  Yu, Xinbo;  Li, Bin;  He, Wei;  Feng, Yanghe;  Cheng, Long;  Silvestre, Carlos
收藏  |  浏览/下载:279/0  |  提交时间:2022/02/16
Robots  Robot sensing systems  Task analysis  Force  Impedance  Sensors  Collaboration  Error constraint  human-robot co-transportation  input constraint  neural networks (NNs)  vision and force sensing  
Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 5, 页码: 3282-3292
作者:  Yang, Chenguang;  Peng, Guangzhu;  Cheng, Long;  Na, Jing;  Li, Zhijun
收藏  |  浏览/下载:263/0  |  提交时间:2021/05/31
Robot sensing systems  Force  Robot kinematics  Artificial neural networks  Admittance  Torque  Admittance control  error transformation  force observer  Kinect  neural adaptive control  neural networks (NNs)  robot  
A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: PP, 期号: 99, 页码: 1-13
作者:  Xiaohu,Zhou;  Xiaoliang.Xie;  Zhenqiu,Feng;  Zengguang,Hou;  Guibin,Bian;  Ruiqi,Li;  Zhenliang,Ni;  Shiqi,Liu;  Yan-Jie Zhou
Adobe PDF(2286Kb)  |  收藏  |  浏览/下载:301/60  |  提交时间:2020/11/05
Endovascular manipulations  multilayer and multimodal-fusion architecture (MMFA)  percutaneous coronary intervention (PCI),  technical skill assessment  
服务机器人导航与抓取检测研究 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2020
作者:  于莹莹
Adobe PDF(22709Kb)  |  收藏  |  浏览/下载:284/22  |  提交时间:2020/09/11
服务机器人  路径态势感知  导航  同时检测分割  遮挡修复  抓取检测  
Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot-Environment Interaction 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2019, 卷号: 49, 期号: 7, 页码: 2568-2579
作者:  Yang, Chenguang;  Peng, Guangzhu;  Li, Yanan;  Cui, Rongxin;  Cheng, Long;  Li, Zhijun
收藏  |  浏览/下载:294/0  |  提交时间:2019/07/11
Admittance control  neural networks (NNs)  observer  optimal adaptive control  robot-environment interaction  
Corridor-scene classifying methods for mobile robot based on multi-sonar-sensor information fusion 期刊论文
International Journal of Information Acquisition, 2007, 卷号: 4, 期号: 1, 页码: 15-26
作者:  Xiuqing Wang;  Zeng-Guang Hou;  Long Cheng;  Min Tan;  Fei Zhu
收藏  |  浏览/下载:99/0  |  提交时间:2019/02/14
Sonar  PCA  kernel PCA  mobile robot  classification  
Neural networks enhancedadaptive admittance control of optimized robot-environment interaction 期刊论文
IEEE Transactions on Cybernetics, 2018
作者:  Chenguang Yang;  Guangzhu Peng;  Yanan Li;  Rongxin Cui;  Long Cheng;  Zhijun Li
收藏  |  浏览/下载:158/0  |  提交时间:2019/02/14
Admittance control  neural networks (NNs)  observer  optimal adaptive control  robot–environment interaction