Averaged controllability of heat equation in unbounded domains with random geometry and location of controls: The Green’s function approach

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Archives of Control Sciences Pub Date : 2023-07-20 DOI:10.24425/ACS.2019.131226
J. Klamka, A. Khurshudyan
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引用次数: 4

Abstract

The constrained averaged controllability of linear one-dimensional heat equation defined on R and R+ is studied. The control is carried out by means of the time-dependent intensity of a heat source located at an uncertain interval of the corresponding domain, the end-points of which are considered as uniformly distributed random variables. Employing the Green’s function approach, it is shown that the heat equation is not constrained averaged controllable neither in R nor in R. Sufficient conditions on initial and terminal data for the averaged exact and approximate controllabilities are obtained. However, constrained averaged controllability of the heat equation is established in the case of point heat source, the location of which is considered as a uniformly distributed random variable. Moreover, it is obtained that the lack of averaged controllability occurs for random variables with arbitrary symmetric density function.
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具有随机几何和控制位置的无界域中热方程的平均可控性:格林函数方法
研究了定义在R和R+上的一维线性热方程的约束平均可控性。控制方法是利用热源的强度随时间的变化,热源的强度位于相应区域的不确定区间,热源的端点作为均匀分布的随机变量。利用格林函数方法,证明了热方程在R和R内均不受约束的平均可控,得到了初始和终端数据的平均精确和近似可控的充分条件。而对于点热源,则建立了热方程的约束平均可控性,将点热源的位置视为均匀分布的随机变量。此外,还得到了具有任意对称密度函数的随机变量缺乏平均可控性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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