道格里斯-尼伦伯格类型状态受限系统的庞特里亚金最大原则

IF 1.6 3区 数学 Q2 MATHEMATICS, APPLIED
Alexey S. Matveev, Dmitrii V. Sugak
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引用次数: 0

摘要

本文主要研究高阶非线性 PDE 系统(其线性近似为 Douglis-Nirenberg 意义上的椭圆)所描述的植物最优控制问题,特别关注其特殊情况:非线性 Navier-Stokes 方程的标准静态系统。我们的目标是建立庞特里亚金最大原理的类似模型。主要挑战来自于对系统状态的无穷多个点向约束,这些约束是在给定自变量连续集的任意点上施加的。我们首先以 "抽象 "最大值原理的形式,为以函数分析语言表述的一般最优控制问题获得了最优性的必要条件。这一结果针对的是一类广泛的问题,其想法是在证明过程中吸收推导最优性条件所需的大量技术工作,这样,在处理特定问题时,基本上只需要对所讨论的结果进行解释。通过对高阶椭圆方程和纳维-斯托克斯方程的状态约束系统进行上述庞特里亚金最大原理的模拟,证明了这种方法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pontryagin’s Maximum Principle for a State-Constrained System of Douglis-Nirenberg Type

This article is concerned with optimal control problems for plants described by systems of high order nonlinear PDE’s (whose linear approximation is elliptic in the sense of Douglis-Nirenberg), with a special attention being given to their particular case: the standard stationary system of non-linear Navier–Stokes equations. The objective is to establish an analog of the Pontryagin’s maximum principle. The major challenge stems from the presence of infinitely many point-wise constraints on the system’s state, which are imposed at any point from a given continuum set of independent variables. Necessary conditions for optimality in the form of an “abstract” maximum principle are first obtained for a general optimal control problem couched in the language of functional analysis. This result is targeted at a wide class of problems, with an idea to absorb, in its proof, a great deal of technical work needed for derivation of optimality conditions so that only an interpretation of the discussed result would be basically needed to handle a particular problem. The applicability of this approach is demonstrated via obtaining the afore-mentioned analog of the Pontryagin’s maximum principle for a state-constrained system of high-order elliptic equations and the Navier–Stokes equations.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
149
审稿时长
9.9 months
期刊介绍: The Journal of Optimization Theory and Applications is devoted to the publication of carefully selected regular papers, invited papers, survey papers, technical notes, book notices, and forums that cover mathematical optimization techniques and their applications to science and engineering. Typical theoretical areas include linear, nonlinear, mathematical, and dynamic programming. Among the areas of application covered are mathematical economics, mathematical physics and biology, and aerospace, chemical, civil, electrical, and mechanical engineering.
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