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Distributed and cooperative estimation of formation flight of unmanned vehicles subject to faults and unreliable information

机译:分布式和协同估计受故障和不可靠信息影响的无人飞行器编队飞行

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In this paper, a novel framework for cooperative estimation of formation flight of unmanned vehicles subject to presence of actuator faults is proposed. This framework is developed based on the notion of sub-observers where within a group of sub-observers each sub-observer is estimating certain states that are conditioned on a given input, output, and state information. We model the overall estimation process by a weighted estimation (WE) digraph. By selecting an appropriate path in the weighted estimation digraph, an assigned supervisor can select and configure a set of sub-observers to successfully estimate all the system states. In presence of large disturbances, noise, and faults certain sub-observers may become invalid (or unreliable), and consequently the supervisor reconfigures the set of sub-observers by selecting a new path in the estimation digraph such that the impacts of these anomalies are confined to only the local estimators. This will prevent the propagation of the uncertainties on the estimation performance of the entire formation flight system. Simulations are conducted on a five satellite formation flight system in deep space where the results confirm the validity of our analytical work.
机译:本文提出了一种新的框架,用于协同估计存在执行器故障的无人驾驶飞机的编队飞行。该框架是基于子观察者的概念开发的,其中,在一组子观察者中,每个子观察者都根据给定的输入,输出和状态信息来估计某些状态。我们通过加权估计(WE)有向图对整个估计过程进行建模。通过在加权估计图中选择适当的路径,指定的主管可以选择和配置一组子观察者,以成功估计所有系统状态。在存在较大的干扰,噪声和故障的情况下,某些子观察者可能会变得无效(或不可靠),因此,主管可以通过在估计图中选择新路径来重新配置子观察者集,以使这些异常的影响得以消除。仅限于本地估计量。这将防止不确定性在整个编队飞行系统的估计性能上传播。在深空的五颗卫星编队飞行系统上进行了仿真,结果证实了我们分析工作的有效性。

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