System Level Parameterizations, constraints and synthesis
System Level Parameterizations, constraints and synthesis
复制标题
系统级参数化、约束和综合
DOI：
10.23919/acc.2017.7963133
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Doyle
中科院分区：
文献类型：
--
作者：
Yuh;N. Matni;J. Doyle
介绍了控制器综合的系统级方法，它由三个部分组成：系统级参数化（SLP）、系统级约束（SLC）和系统级综合（SLS）问题。SLP提供了一种新的参数化的所有内部稳定控制器和系统的响应，他们实现。这些可以与SLC相结合，以提供约束稳定控制器的参数化。我们提供了一个有用的SLC目录，并表明，通过使用SLPs与SLC，我们可以parameterized最大的已知类的约束稳定控制器，承认凸表征。最后，我们制定的SLS问题，并表明它定义了最广泛的已知类的约束最优控制问题，可以使用凸规划来解决。最后，我们使用系统级的方法来计算探索控制器性能，架构成本，鲁棒性和合成/实现复杂性的权衡。
We introduce the system level approach to controller synthesis, which is composed of three elements: System Level Parameterizations (SLPs), System Level Constraints (SLCs) and System Level Synthesis (SLS) problems. SLPs provide a novel parameterization of all internally stabilizing controllers and the system responses that they achieve. These can be combined with SLCs to provide parameterizations of constrained stabilizing controllers. We provide a catalog of useful SLCs, and show that by using SLPs with SLCs, we can parameterize the largest known class of constrained stabilizing controllers that admit a convex characterization. Finally, we formulate the SLS problem, and show that it defines the broadest known class of constrained optimal control problems that can be solved using convex programming. We end by using the system level approach to computationally explore tradeoffs in controller performance, architecture cost, robustness and synthesis/implementation complexity.