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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：32/0 \| 提交时间：2021/03/11 Hovering control light weight algorithm development image processing self-position estimation small aerial robot tile corner sensing
	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 Dynamic Hovering Visual Servo Control Undulatory Fin Image Feature Neural Network.
	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: Jiang Z(姜哲); Han JD(韩建达); Wang YC(王越超); Song Q(宋崎) 收藏 \| 浏览/下载：27/0 \| 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.