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	Modeling and controller design of hydraulic rotorcraft aerial manipulator 会议论文 OAI收割 27th Chinese Control and Decision Conference, CCDC 2015, Qingdao, China, May 23-25, 2015 作者: Lin TY(林天宇); Li, Yongzhe; Qi JT(齐俊桐); Meng XD(孟祥冬); Han JD(韩建达) 收藏 \| 浏览/下载：25/0 \| 提交时间：2015/11/18 RAM Hydraulic manipulator Modeling LQR controller Robust controller
	A fuzzy control method based on information integration for double inverted pendulum (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者: Fan Y. 收藏 \| 浏览/下载：61/0 \| 提交时间：2013/03/25 This article proposes a new fuzzy controller based on information integration. The mathematical model of Linear double inverted pendulum has been studied and estabLished with dynamics analytical method and LQR theory is used to design the optimal Linear inverted pendulum controller then the integration technology is used to design the variable parameters self-tuning fuzzy controller. Thereby the fuzzy controller input variable dimension and the number of fuzzy control rules have been extremely reduced. Two controllers are designed for inverted pendulum system control and the comparison simulation experiments have been done. The results show that the controllers can both reaLize good control and the fuzzy controller has higher precision faster response better stabiLity and robustness. 2011 IEEE.
	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：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.