中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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浏览/检索结果: 共6条,第1-6条 帮助

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A Study on Hovering Control of Small Aerial Robot by Sensing Existing Floor Features 期刊论文  OAI收割
IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 4, 页码: 1016-1025
作者:  
Chinthaka Premachandra;  Dang Ngoc Hoang Thanh;  Tomotaka Kimura;  Hiroharu Kawanaka
  |  收藏  |  浏览/下载:31/0  |  提交时间:2021/03/11
Image Dynamics-Based Visual Servoing for Quadrotors Tracking a Target With a Nonlinear Trajectory Observer 期刊论文  OAI收割
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 卷号: 50, 期号: 1, 页码: 376-384
作者:  
Cao, Zhiqiang;  Chen, Xuchao;  Yu, Yingying;  Yu, Junzhi;  Liu, Xilong
  |  收藏  |  浏览/下载:40/0  |  提交时间:2020/03/30
Vision-Based Autonomous Hovering for the Biomimetic Underwater Robot-RobCutt-II 期刊论文  OAI收割
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 11, 页码: 8578-8588
作者:  
Wang, Rui;  Wang, Shuo;  Wang, Yu;  Cai, Mingxue;  Tan, Min
  |  收藏  |  浏览/下载:80/0  |  提交时间:2019/12/16
Visual servo control for dynamic hovering of an underwater biomimetic vehicle-manipulator system by neural network 会议论文  OAI收割
Takamatsu, Japan, 2017.8.6—2017.8.9
作者:  
Rui Wang;  Yu Wang;  Shuo Wang;  Chong Tang;  Min Tan
  |  收藏  |  浏览/下载:31/0  |  提交时间:2018/05/31
Aerodynamics of hovering flight in bat 会议论文  OAI收割
23rd International Congress of Theoretical and Applied Mechanics, 中国北京/Beijing, China, 2012-08-19
作者:  
Wang SZ(王士召);  He GW(何国威);  Zhang X(张星)
收藏  |  浏览/下载:24/0  |  提交时间:2014/04/02
Enhanced LQR control for unmanned helicopter in hover 会议论文  OAI收割
1st International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, China, January 19-21, 2006
作者:  
收藏  |  浏览/下载:24/0  |  提交时间:2012/06/06
Real time adaptability is of central importance for the control of Unmanned Helicopter flying under different circumstances. In this paper  an active model is employed to handle the time varying uncertainties involved in the helicopter dynamics during flight. In the scheme  a normal LQR control designed from a simplified model at hovering is enhanced by means of Unscented-Kalman-Filter (UKF) based estimation  which tries to online capture the error between the simplified model and the full dynamics. This is intended to achieve adaptive performance without the need of adjusting the controller modes or parameters along with the changing dynamics of helicopter. Simulations with respect to a model helicopter are conducted to verify both the UKF-based estimation and the enhanced LQR control. Results are also demonstrated with the normal LQR control with the active model enhancement.