Adaptive Unscented Kalman Filter for Estimation of Modelling Errors for Helicopter

摘要：

In order to overcome the drawback of the normal unscented Kalman filter (UKF) a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and modelling errors for helicopter. The filter is composed of two parallel master-slave UKFs, while the master UKF estimates the states/parameters and the slave one estimates the diagonal elements of the noise covariance matrix for the master UKF. Such a mechanism improves the adaptive ability of the UKF and enlarges its application scope. Simulations conducted on the dynamics of helicopter indicate that the performance of the adaptive UKF is superior to the standard one in terms of fast convergence and estimation accuracy.

作者: Qi Song Yuqing He

作者单位: Automation Department,Shenyang Institute of Aeronautical Engineering,37＃ Daoyi South street,Shenyang Robotics Laboratory,Shenyang Institute of Automation,Chinese Academy of Sciences,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 2463-2467

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）