A singular value maximizing data recording algorithm for concurrent learning
A singular value maximizing data recording algorithm for concurrent learning
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DOI：
10.1109/acc.2011.5991481
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Girish V. Chowdhary;Eric N. Johnson
中科院分区：
文献类型：
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作者：
Girish V. Chowdhary;Eric N. Johnson
我们提出了一个奇异值最大化算法记录数据使用的并发学习自适应控制器。这些控制器同时使用记录的和当前的数据，并且可以具有指数稳定性保证，收敛速度与包含记录数据的矩阵的最小奇异值成正比。所提出的算法选择记录的数据，以提高最小奇异值，从而提高跟踪性能的结果，这是建立通过与以前研究的数据记录方法，记录点是足够不同的比较。
We present a singular value maximizing algorithm for recording data to be used by concurrent learning adaptive controllers. These controllers use recorded and current data concurrently and can have exponential stability guarantees, with the rate of convergence directly proportional to the minimum singular value of the matrix containing recorded data. The presented algorithm selects data for recording to improve the minimum singular value, and hence results in improved tracking performance, this is established through comparison with previously studied data recording methods that record points that are sufficiently different.