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	Active Model-Based Predictive Control and Experimental Investigation on Unmanned Helicopters in Full Flight Envelope 期刊论文 OAI收割 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 卷号: 21, 期号: 4, 页码: 1502-1509 作者: Song DL(宋大雷); Han JD(韩建达); Liu GJ(刘光军) 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/10/05 Active model-based control modeling error elimination predictive control unmanned helicopter
	Trajectory tacking control of a quad-rotor based on active disturbance rejection control (EI CONFERENCE) 会议论文 OAI收割 2012 IEEE International Conference on Automation and Logistics, ICAL 2012, August 15, 2012 - August 17, 2012, Zhengzhou, China Gong X.; Tian Y.; Bai Y.; Zhao C. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 The objective of this paper is to deal with a trajectory tracking of a Quad-rotor unmanned aerial vehicle (UAV). For the model uncertainty the external disturbance and the coupling factor are considered an active disturbance rejection control (ADRC) algorithm is introduced into the designing procedure. The aircraft dynamic model is proposed in this article based on which the closed-loop control system is divided into four independent channels with the coupling factor compensated by the extended state observer (ESO). The nonlinear state error feedback (NLSEF) algorithm is designed in each channel to improve the closed-loop dynamics. In this article the ADRC controller is expressed in the discrete form. And finally the simulation results show that the proposed control algorithm achieves a favourable tracking performance. 2012 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.