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	Active modeling for pneumatic artificial muscle 会议论文 OAI收割 14th IEEE International Workshop on Advanced Motion Control, AMC 2016, Auckland, New zealand, April 22-24, 2016 作者: Zhang DH(张道辉); Zhao XG(赵新刚); Han JD(韩建达) 收藏 \| 浏览/下载：44/0 \| 提交时间：2016/08/23 active modeling generalized three-element model model error estimation, Kalman filter pneumatic artifial muscle
	A Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation SCI/SSCI论文 OAI收割 2016 作者: Chen S. H. 收藏 \| 浏览/下载：30/0 \| 提交时间：2016/12/16 A trous wavelet transform ensemble Kalman filter evapotranspiration four-dimensional variational assimilation generalized Gaussian distribution uncertainty estimation variational data assimilation ensemble kalman filter error covariances model equations parameter
	A Trous Wavelet Based Four-Dimensional Evapotranspiration Assimilation SCI/SSCI论文 OAI收割 2016 作者: Chen S. H. \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2017/11/09 A trous wavelet transform ensemble Kalman filter evapotranspiration four-dimensional variational assimilation generalized Gaussian distribution uncertainty estimation variational data assimilation ensemble kalman filter error covariances model equations parameter
	Application of adaptive Kalman filter technique in initial alignment of strapdown inertial navigation system (EI CONFERENCE) 会议论文 OAI收割 29th Chinese Control Conference, CCC'10, July 29, 2010 - July 31, 2010, Beijing, China 作者: Liu P. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 In order to improve the alignment precision and convergence speed of strap-down inertial navigation system but in the active system most noise statistical characteristics are unknown an initial alignment method based on Sage-Husa adaptive filter is presented. We also derived the exactitude alignment error model and adaptive Kalman filter equation in the azimuth of small misalignment angle. As usual in this case known the noise statistical characteristics we introduce the adaptive Kalman filter. It uses the information of observed data Kalman filter is suitable on-line estimation noise statistical characteristics and state simultaneously in order to improve the filter continuously so the filter has a higher estimation accuracy than the conventional Kalman filter. By simulating verifying the adaptive Kalman filter enhances the convergence speed and alignment accuracy effectively.
	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.
	INS's error compensation on the base of the celestial theodolite (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Devices and Instruments, August 21, 2005 - August 26, 2005, Changchun, China 作者: Guo L.-H.; Zhao H.-B. 收藏 \| 浏览/下载：70/0 \| 提交时间：2013/03/25 In recent years there has been a major upsurge of interest in the integrated inertial navigation system (INS)/celestial navigation system (CNS) as a cost-effective way of providing accurate and reliable navigation aid for civil and military vehicles (ships aircrafts land vehicles and so on). One of the disadvantages of INS is its errors will grow unbounded. The CNS can be used to improve position estimation resulting from INS measurement. This paper describes the design of this. An error model developed earlier is used for CNS/INS filter (Kalman filter) mechanization. In CNS celestial theodolite acquires an image of the sky recognize the most brilliant stars in the image creates with them a "constellation" and searches for this pattern in an on board star catalogue of the observed region to get the precise position and attitude information of vehicles. The Kalman filter method is used to fuse measurement from the system. We can use this information to compensate INS's error. The tests carry out with this system show that system will get accurate navigation information.