Solving large-scale control problems
Solving large-scale control problems
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DOI：
10.1109/mcs.2004.1272745
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发表时间：
2004-06
期刊：
影响因子：
--
通讯作者：
P. Benner
中科院分区：
文献类型：
--
作者：
P. Benner
在这篇文章中，我们讨论稀疏矩阵算法和并行算法，以及它们在大规模系统中的应用。为了说明，我们解决了线性二次调节器（LQR）的问题，并应用平衡截断模型减少使用并行计算或稀疏矩阵算法。我们的结论是，现代工具，从数值线性代数，沿着仔细调查和开发的问题结构，可以用来推导出能够解决大型控制问题的算法。由于这些方法是在生产质量的软件中实现的，控制工程师可以采用复杂的模型，并使用计算工具来分析和设计反馈控制律。
In this article we discuss sparse matrix algorithms and parallel algorithms, as well as their application to large-scale systems. For illustration, we solve the linear-quadratic regulator (LQR) problem and apply balanced truncation model reduction using either parallel computing or sparse matrix algorithms. We conclude that modern tools from numerical linear algebra, along with careful investigation and exploitation of the problem structure, can be used to derive algorithms capable of solving large control problems. Since these approaches are implemented in production-quality software, control engineers can employ complex models and use computational tools to analyse and design feedback control laws.