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专利名称: 一种基于UKF的水下机器人状态和参数联合估计方法
其他题名: Unscented Kalman filter (UKF)-based underwater robot state and parameter joint estimation method
作者: 刘开周; 程大军; 李一平; 封锡盛
所属部门: 水下机器人技术研究室
专利权人: 中国科学院沈阳自动化研究所
专利代理: 沈阳科苑专利商标代理有限公司 21002
专利国别: 中国
专利类型: 发明授权
专利状态: 有效
摘要: 本发明公开一种基于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包括水下机器人的动力学模型和推进器的故障模型。本发明依据位置传感器探测到的位姿信息,采用UKF算法,以水下机器人状态包括位姿和速度及推进器故障参数,对扩展参考模型进行在线联合估计,实时估计出水下机器人的速度信息和推进器故障信息。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;当过程噪声和测量噪声的先验信息已知的情况下,该方法能够达到较高的估计精度。
英文摘要: The invention discloses an unscented Kalman filter (UKF)-based underwater robot state and parameter joint estimation method. According to the method, expansion reference models of an underwater robot are established, and comprise a kinetic model of the underwater robot and a fault model of a propeller. According to pose information detected by a position sensor, the expansion reference models are subjected to on-line joint estimation through states of the underwater robot, including pose and speed, and propeller fault parameters by a UKF algorithm, and the speed information of the underwater robot and the propeller fault information are estimated in real time. The method has a high real-time property, and the states and parameters of a system can be subjected to joint estimation; and under the condition that prior information of process noise and measurement noise is known, high estimation accuracy can be achieved by the method.
是否PCT专利:
申请日期: 2011-05-25
公开日期: 2012-11-28
授权日期: 2015-03-11
专利申请号: CN201110137339.2
公布/公告号: CN102795323B
语种: 中文
产权排序: 1
内容类型: 专利
URI标识: http://ir.sia.cn/handle/173321/15871
Appears in Collections:水下机器人研究室_专利

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Recommended Citation:
刘开周,程大军,李一平,等. 一种基于UKF的水下机器人状态和参数联合估计方法. CN102795323B. 2012.
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格式: Adobe PDF
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