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科研机构
自动化研究所 [17]
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期刊论文 [12]
学位论文 [5]
发表日期
2023 [1]
2022 [1]
2021 [1]
2016 [2]
2015 [5]
2011 [1]
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专题:自动化研究所
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Appointed-Time Control for Flexible Hypersonic Vehicles with Conditional Disturbance Negation
期刊论文
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2023, 卷号: 59, 期号: 5, 页码: 6327-6345
作者:
Sun, Jinlin
;
Pu, Zhiqiang
;
Chang, Yafei
;
Ding, Shihong
;
Yi, Jianqiang
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浏览/下载:0/0
  |  
提交时间:2024/02/22
Vehicle dynamics
Convergence
Control systems
Aerodynamics
Closed loop systems
Actuators
Mathematical models
Appointed-time control
conditional disturbance negation
hypersonic vehicle
input saturation
prescribed performance control
Two-Level Energy Control Strategy Based on ADP and A-ECMS for Series Hybrid Electric Vehicles
期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 12
作者:
Shen, Zhen
;
Luo, Can
;
Dong, Xisong
;
Lu, Wanze
;
Lv, Yisheng
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  |  
浏览/下载:50/0
  |  
提交时间:2022/03/17
Hybrid electric vehicles
Fuels
Electronic countermeasures
Minimization
Mechanical power transmission
Optimization
Energy management
Adaptive dynamic programming
adaptive-equivalent consumption minimization strategy
hybrid electric vehicle
energy control
Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles
期刊论文
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 卷号: 57, 期号: 2, 页码: 1028-1045
作者:
Chai, Runqi
;
Tsourdos, Antonios
;
Al Savvaris
;
Wang, Shuo
;
Xia, Yuanqing
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  |  
浏览/下载:28/0
  |  
提交时间:2021/06/07
Unmanned aerial vehicles
Probabilistic logic
Trajectory optimization
Mathematical model
Aerodynamics
Vehicle dynamics
Trajectory planning
Chance constrained
convex approximation
convex optimization
convexification
trajectory optimization
unmanned vehicle
基于高斯过程的强化学习及汽车智能巡航控制
学位论文
工学博士, 北京: 中国科学院研究生院, 2016
作者:
夏中谱
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浏览/下载:84/0
  |  
提交时间:2016/06/15
强化学习控制,高斯过程,连续状态系统,无模型控制,智能巡航控制
Development of an Underwater Manipulator and Its Free-Floating Autonomous Operation
期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2016, 卷号: 21, 期号: 2, 页码: 815-824
作者:
Wang, Yu
;
Wang, Shuo
;
Wei, Qingping
;
Tan, Min
;
Zhou, Chao
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2016/06/14
Autonomous operation
free-floating manipulation
underwater manipulator
UVMS
A Loss-Minimization Port-Controlled Hamilton Scheme of Induction Motor for Electric Vehicles
期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2015, 卷号: 20, 期号: 6, 页码: 2645-2653
作者:
Yu, Junzhi
;
Pei, Wenhui
;
Zhang, Chenghui
;
Yu, Junzhi
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  |  
浏览/下载:17/0
  |  
提交时间:2016/01/18
Electric vehicles (EVs)
induction motor
iron loss
loss minimization
port controlled hamiltonian
Path Generation of Autonomous Approach to a Moving Ship for Unmanned Vehicles
期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 卷号: 62, 期号: 9, 页码: 5619-5629
作者:
Wang, Yu
;
Wang, Shuo
;
Tan, Min
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  |  
浏览/下载:23/0
  |  
提交时间:2015/09/23
Autonomous approach
path generation
trajectory smoothing
unmanned vehicles
A Class of Adaptive Extended State Observers for Nonlinear Disturbed Systems
期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 卷号: 62, 期号: 9, 页码: 5858-5869
作者:
Pu, Zhiqiang
;
Yuan, Ruyi
;
Yi, Jianqiang
;
Tan, Xiangmin
收藏
  |  
浏览/下载:38/0
  |  
提交时间:2015/09/23
Active disturbance rejection control
extended state observer (ESO)
PD-eigenvalue
stability analysis
time varying
Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 4, 页码: 684-696
作者:
Luo, Biao
;
Wu, Huai-Ning
;
Li, Han-Xiong
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2016/03/30
Adaptive optimal control
empirical eigenfunction (EEF)
highly dissipative partial differential equations (PDEs)
neuro-dynamic programming (NDP)
spatially distributed processes (SDPs)
A Novel Centralized TDMA-Based Scheduling Protocol for Vehicular Networks
期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 1, 页码: 411-416
作者:
Zhang, Rongqing
;
Cheng, Xiang
;
Yang, Liuqing
;
Shen, Xia
;
Jiao, Bingli
收藏
  |  
浏览/下载:48/0
  |  
提交时间:2015/09/18
Resource reusing
scheduling protocol
time-division multiple access (TDMA)
vehicle-to-infrastructure (V2I)
vehicle-to-vehicle (V2V)
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