含有有偏测量误差的相似编队算法
首发时间:2017-05-03
摘要:本文研究了多主体系统的分布式相似编队算法。在理想条件下,相似编队算法被证明是收敛的。但是所有的相似编队算法在实际应用时都不可避免的伴随着测量噪声和运动噪声。在考虑有偏测量噪声的情况下,原有的相似编队算法不能保证节点收敛到期望的队形。本文提出了一种改进的相似编队算法,这种算法可以帮助节点克服有偏测量误差和无偏运动误差的影响,在均值意义上收敛到期望队形,并且队形误差在均方意义上一致有界。
关键词: 分布式队形控制,有偏测量误差,均值可达性,多主体系统
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Similar Formation Algorithm With Biased Measurement Error
Abstract:This paper studies the distributed similar formation algorithm for multi-agent systems. In the ideal condition, the similar formation algorithm is proved convergent. However, any formation algorithm used in practice is inevitably accompanied by measurement noise and motion noise. With biased measurement error, the original similar formation algorithm cannot ensure the agents to reach a desired formation shape. This paper proposes a modified similar formation algorithm, with which the agents can overcome biased measurement error and unbiased motion noise to reach the desired formation shape in the expectation sense, and the formation error is globally uniformly bounded in the mean square sense.
Keywords: Distributed formation control, Biased measurement error, Average accessibility, Multi-agent systems
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