sss & sssMOR: Analysis and reduction of large-scale dynamic systems in MATLAB
sss & sssMOR: Analysis and reduction of large-scale dynamic systems in MATLAB
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DOI：
10.1515/auto-2016-0137
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发表时间：
2017-02
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影响因子：
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通讯作者：
A. Castagnotto;M. C. Varona;Lisa Jeschek;B. Lohmann
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文献类型：
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作者：
A. Castagnotto;M. C. Varona;Lisa Jeschek;B. Lohmann
摘要：我们提出了两个MATLAB工具箱，作为开源代码提供，扩展控制系统的能力，以大规模的模型。SSS允许利用重新访问的函数（例如Bode、Step、Norm等）来定义和分析稀疏状态空间（SSS）对象，以利用系统矩阵的稀疏性。sssMOR需要模型简化算法，该算法在显著较低维度的模型中捕获高阶系统的相关动态。sssMOR_App提供了一个图形用户界面，便于与工具进行交互。使用sss和sssMOR可以分析状态空间维数高于O（104）的动态系统，这通常是内置ss对象的极限。在这篇文章中，我们首先介绍了工具箱和主要功能。数值算例显示了使用该工具的优点。
Abstract We present two MATLAB toolboxes, provided as open-source code, that expand the capabilities of the Control System Toolbox to large-scale models. sss allows the definition and analysis of sparse state-space (sss) objects with functions (such as bode, step, norm,…) revisited to exploit the sparsity of the system matrices. sssMOR entails model reduction algorithms that capture the relevant dynamics of high order systems in models of significantly lower dimensions. The sssMOR_App provides a graphical user interface for easy interaction with the tools. With sss and sssMOR it is possible to analyze dynamical systems with state-space dimensions higher than O(104), which is typically the limit for built-in ss objects. In this contribution, we give a first introduction to the toolboxes and the main functionality. Numerical examples show the advantages of using the tools.