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一种基于自适应UKF的水下机器人状态和参数联合估计方法
Alternative TitleUnderwater robot state and parameter joint estimation method based on self-adaption unscented Kalman filtering (UKF)
刘开周; 程大军; 李一平; 封锡盛
Department水下机器人研究室
Rights Holder中国科学院沈阳自动化研究所
Patent Agent沈阳科苑专利商标代理有限公司 21002
Country中国
Subtype发明
Status有权
Abstract本发明公开一种基于自适应UKF的水下机器人状态和参数联合估计方法,该方法首先建立了水下机器人的扩展参考模型,该参考模型有水下机器人的动力学模型和推进器的故障模型;依据位置传感器探测到的位姿信息,采用自适应UKF的主滤波器对水下机器人状态包括位姿和速度及推进器故障组成的扩展状态传递和更新,实时估计出水下机器人的速度信息和推进器故障信息;同时,依据主滤波器的新息信息,采用自适应UKF的辅助滤波器对系统的噪声信息进行实时的估计。该方法具有很好的实时性,可在线对系统的状态和参数进行联合估计,且当过程噪声和测量噪声的先验信息未知的情况下,该方法也能够达到较的估计精度。
Other AbstractThe invention discloses an underwater robot state and parameter joint estimation method based on self-adaption unscented Kalman filtering (UKF). The method comprises building an expanding reference model of an underwater robot, and enabling the reference model to have a dynamical model of the underwater robot and a fault model of a thruster; adopting a main filter of the self-adaption UKF to deliver and update expanding states composed of poses and speed of the underwater robot state and faults of the thruster according to pose information detected by a position sensor, and timely estimating speed information of the underwater robot and fault message of the thruster; and simultaneously adopting an accessory filter of the self-adaption UKF to timely estimate noise information of a system according to innovation information of the main filter. The underwater robot state and parameter joint estimation method has good instantaneity, can conduct joint estimation on states and parameters of the system, and can achieve high estimation accuracy when priori information of process noise and measurement noise is unknown.

 

PCT Attributes
Application Date2011-07-08
2013-01-09
Date Available2014-12-10
Application NumberCN201110190512.5
Open (Notice) NumberCN102862666A
Language中文
Contribution Rank1
Document Type专利
Identifierhttp://ir.sia.cn/handle/173321/13092
Collection水下机器人研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
刘开周,程大军,李一平,等. 一种基于自适应UKF的水下机器人状态和参数联合估计方法[P]. 2013-01-09.
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