Subspace-based methods for the identification of linear time-invariant systems
Subspace-based methods for the identification of linear time-invariant systems
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DOI：
10.1016/0005-1098(95)00107-5
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发表时间：
1995-12
期刊：
影响因子：
--
通讯作者：
M. Viberg
中科院分区：
文献类型：
--
作者：
M. Viberg
基于子空间的系统辨识方法在过去的几年中引起了人们的广泛关注。这种兴趣是由于直接从输入输出数据提供多变量线性系统的精确状态空间模型的能力。这些方法起源于20世纪60年代发展起来的经典状态空间实现理论。主要的计算工具是QR和奇异值分解。在这里，现有的基于子空间的系统识别技术的概述。这些方法被分为基于实现的和直接技术的类。不同的算法之间的相似之处，并指出其适用性的评论。我们还讨论了一些最近的想法，改进和扩展的方法。最后给出了一个仿真算例，对不同的算法进行了比较.基于子空间的方法被发现执行竞争相对于预测误差的方法，提供了系统被适当地激发。
Subspace-based methods for system identification have attracted much attention during the past few years. This interest is due to the ability of providing accurate state-space models for multivariable linear systems directly from input-output data. The methods have their origin in classical state-space realization theory as developed in the 1960s. The main computational tools are the QR and the singular-value decompositions. Here, an overview of existing subspace-based techniques for system identification is given. The methods are grouped into the classes of realization-based and direct techniques. Similarities between different algorithms are pointed out, and their applicability is commented upon. We also discuss some recent ideas for improving and extending the methods. A simulation example is included for comparing different algorithms. The subspace-based approach is found to perform competitive with respect to prediction-error methods, provided the system is properly excited.