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Design and modeling of a bioinspired flexible finger exoskeleton for strength augmentation 期刊论文
IEEE/ASME Transactions on Mechatronics, 2024, 页码: 1-12
作者:  Li, Guotao;  Cheng, Long;  Zhang, Can
Adobe PDF(4765Kb)  |  收藏  |  浏览/下载：25/7  |  提交时间：2024/04/25
Bionic Underwater Vehicle: A Data-Driven Disturbance Rejection Control Framework 期刊论文
IEEE ROBOTICS & AUTOMATION MAGAZINE, 2023, 卷号: 31, 期号: 1, 页码: 18-28
作者:  Wang, Kaihui;  Zou, Wei;  Ma, Ruichen;  Lv, Jiaqi;  Su, Hu;  Wang, Yu;  Ma, Hongxuan
Adobe PDF(2970Kb)  |  收藏  |  浏览/下载：42/0  |  提交时间：2024/02/22
Vehicle dynamics  Robots  Propulsion  Predictive models  Biological system modeling  Robustness  Disturbance observers  
仿豹魴鮄机器人设计与控制策略学习 学位论文
, 2023
作者:  张天栋
Adobe PDF(32276Kb)  |  收藏  |  浏览/下载：184/10  |  提交时间：2023/06/22
仿生水下机器人  人工侧线传感器  控制策略学习  强化学习  课程学习  
Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform 期刊论文
International Journal of Control, Automation and Systems, 2022, 卷号: 20, 期号: 3, 页码: 968-982
作者:  Tamir, Tariku Sinshaw;  Xiong,Gang;  Dong,Xisong;  Fang,Qihang;  Liu,Sheng;  Lodhi,Ehtisham;  Shen,Zhen;  Wang, Fei-Yue
Adobe PDF(3410Kb)  |  收藏  |  浏览/下载：366/171  |  提交时间：2022/04/07
Additive Manufacturing, Extended Proportion-Derivation Sliding Mode Controller, Grey Wolf Optimization, Print Quality, Stewart Platform  
Design and Locomotion Control of a Dactylopteridae-Inspired Biomimetic Underwater Vehicle With Hybrid Propulsion 期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 页码: 13
作者:  Zhang, Tiandong;  Wang, Rui;  Wang, Yu;  Cheng, Long;  Wang, Shuo;  Tan, Min
Adobe PDF(4770Kb)  |  收藏  |  浏览/下载：260/45  |  提交时间：2022/01/27
Propulsion  DC motors  Robots  Control systems  Servomotors  Underwater vehicles  Sports  Biomimetic underwater vehicle (BUV)  body andor caudal fin (BCF)  median and or paired fin (MPF) hybrid propulsion  central pattern generators (CPGs)  fuzzy proportion integral differential (PID)  
Online reinforcement learning for continuous-state systems 专著章节/文集论文
出自: Frontiers of Intelligent Control and Information Processing, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore:World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, 2014
作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载：249/27  |  提交时间：2017/09/13
Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2016, 卷号: 10, 期号: 12, 页码: 1339-1347
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Li, Xiangjun
浏览  |  Adobe PDF(976Kb)  |  收藏  |  浏览/下载：407/167  |  提交时间：2016/12/26
Nonlinear Control Systems  Continuous Time Systems  Learning (Artificial Intelligence)  Optimal Control  Dynamic Programming  Lyapunov Methods  Linear Systems  Reinforcement Learning  Continuous-time Problem  Nonlinear Optimal Tracking Problem  Adaptive Dynamic Programming  Model-free Adaptive Optimal Tracking Algorithm  Lyapunov Analysis  Linear System  
Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 4, 页码: 851-865
作者:  Song, Ruizhuo;  Lewis, Frank;  Wei, Qinglai;  Zhang, Hua-Guang;  Jiang, Zhong-Ping;  Levine, Dan;  Qinglai Wei
浏览  |  Adobe PDF(3455Kb)  |  收藏  |  浏览/下载：408/176  |  提交时间：2015/09/21
Actor-critic  Approximate Dynamic Programming (Adp)  Category  Optimal Control  Shunting Inhibitory Artificial Neural Network (Siann)  
Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2009, 卷号: 39, 期号: 3, 页码: 788-799
作者:  Xu, Dong;  Zhao, Dongbin;  Yi, Jianqiang;  Tan, Xiangmin
浏览  |  Adobe PDF(443Kb)  |  收藏  |  浏览/下载：249/90  |  提交时间：2015/08/12
Omnidirectional Mobile Manipulators  Robust Neural Network (Nn)  Sliding Mode Control (Smc)  Trajectory Tracking Control  Uncertainties