机构

• 沈阳自动化研究所 [2]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [2]
• 期刊论文 [1]

发表日期

• 2010 [1]
• 2008 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Application of adaptive UKF in initial alignment of MINS/GPS integrated navigation system (EI CONFERENCE) 会议论文  OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者:  Gao Q. 收藏  |  浏览/下载：32/0  |  提交时间：2013/03/25 In order to overcome the shortcomings of standard unscented Kalman filter (UKF)  which are obviously influenced by the error of initial value and the model error of system  adaptive UKF which is based on the adaptive principle is applied in initial alignment of the MINS/GPS integrated navigation system. Coarse alignment cannot be done by micro inertial measurement unit (MIMU) itself because of its low precision  the method of using magnetometer to assist it with coarse alignment is presented. Simulation results show that the adaptive UKF can overcome the influences of initial values error and inaccurate system model  and improve the convergence speed and alignment accuracy effectively. 2010 IEEE.   An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot 期刊论文  OAI收割 自动化学报, 2008, 卷号: 34, 期号: 1, 页码: 72-79 作者:  Song Q(宋崎);  Han JD(韩建达) 收藏  |  浏览/下载：18/0  |  提交时间：2012/05/29 Adaptive Unscented Kalman filter (UKF)  innovation  MIT rule  process covariance   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.