Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function
Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function
复制标题
控制辨识：关于最坏情况 $ u$-gap 成本函数的最优输入设计
DOI：
10.1137/s0363012901399866
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
M. Gevers
中科院分区：
文献类型：
--
作者：
R. Hildebrand;M. Gevers
参数识别实验提供了一个已识别的模型，在参数空间中的椭球形不确定性区域。因此，鲁棒控制器设计的目标是稳定所有的植物在确定的不确定性区域。目前的贡献的主题是设计一个识别实验，使最坏情况下的$\nu$-差距在所有的植物在所产生的不确定性区域之间的确定的植物和植物在这个区域是尽可能小。通过输入功率谱优化进行实验设计。两个成本函数进行了研究，这代表了不同程度的准确性和计算复杂性之间的权衡。结果表明，这些成本函数的输入优化问题是服从凸分析中使用的标准数值算法。
Parameter identification experiments deliver an identified model together with an ellipsoidal uncertainty region in parameter space. The objective of robust controller design is thus to stabilize all plants in the identified uncertainty region. The subject of the present contribution is to design an identification experiment such that the worst-case $\nu$-gap over all plants in the resulting uncertainty region between the identified plant and plants in this region is as small as possible. The experiment design is performed via input power spectrum optimization. Two cost functions are investigated, which represent different levels of trade-off between accuracy and computational complexity. It is shown that the input optimization problem with respect to these cost functions is amenable to standard numerical algorithms used in convex analysis.