An approach to identification for robust control
An approach to identification for robust control
复制标题
鲁棒控制的识别方法
DOI：
10.1109/tac.2003.812821
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Huipin Zhang
中科院分区：
文献类型：
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作者：
S. Gugercin;A. Antoulas;Huipin Zhang
给定由离散线性系统产生的测量数据，我们提出了一个由范数有界摄动影响的线性时不变系统组成的模型。在温和的假设下，属于不确定科的植物形成一个凸集。该方法依赖于两个关键参数：一个先验的给定扰动边界和用于生成数据的输入。结果表明，利用不同输入得到的模型族相交，可以减小不确定族的规模。分析了该识别方案中的模型验证问题。对于给定的能量水平，失效问题产生了那些在任何可能的固定能量输入和任何可能的扰动下永远不会失效的模型族；这导致了所有不确定家庭的交集。无效问题的一个后果是，对于有限长度的测量，使用固定能量输入，并非所有模型都可以无效。
Given measured data generated by a discrete-time linear system, we propose a model consisting of a linear time-invariant system affected by norm-bounded perturbation. Under mild assumptions, the plants belonging to the resulting uncertain family form a convex set. The approach depends on two key parameters: an a priori given bound of the perturbation and the input used to generate the data. It turns out that the size of the uncertain family can be reduced by intersecting the model families obtained by making use of different inputs. The model validation problem in this identification scheme is analyzed. For a given energy level, the invalidation problem yields the family of those models which can never be invalidated for any possible input of fixed energy and any possible perturbation; this leads to the intersection of all uncertain families. A consequence of the invalidation problem is that for finite length measurements not all models can be invalidated, using fixed-energy inputs.