A priori and a posteriori error estimates of finite‐element approximations for elliptic optimal control problem with measure data

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Optimal Control Applications & Methods Pub Date : 2018-11-19 DOI:10.1002/oca.2476
Pratibha Shakya, R. K. Sinha
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引用次数: 3

Abstract

We analyze both a priori and a posteriori error analysis of finite‐element method for elliptic optimal control problems with measure data in a bounded convex domain in Rd (d = 2or3). The solution of the state equation of such type of problems exhibits low regularity due to the presence of measure data, which introduces some difficulties for both theory and numerics of the finite‐element method. We first prove the existence, uniqueness, and regularity of the solution to the optimal control problem. To discretize the control problem, we use continuous piecewise linear elements for the approximations of the state and co‐state variables, whereas piecewise constant functions are used for the control variable. We derive a priori error estimates of order O(h2−d2) for the state, co‐state, and control variables in the L2‐norm. Further, global a posteriori upper bounds for the state, co‐state, and control variables in the L2‐norm are established. Moreover, local lower bounds for the errors in the state and co‐state variables and a global lower bound for the error in the control variable are obtained in the case of two space dimensions (d = 2). Numerical experiments are provided, which support our theoretical results.
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具有测量数据的椭圆型最优控制问题有限元逼近的先验和后验误差估计
本文对有界凸域上具有测量数据的椭圆型最优控制问题的有限元方法进行了先验和后验误差分析。由于测量数据的存在,这类问题的状态方程的解具有较低的规律性,这给有限元法的理论和数值计算带来了一定的困难。首先证明了最优控制问题解的存在性、唯一性和正则性。为了使控制问题离散化,我们使用连续分段线性元素来逼近状态变量和协状态变量,而使用分段常数函数来逼近控制变量。我们为L2 -范数中的状态、共状态和控制变量导出了O(h2−d2)阶的先验误差估计。进一步,建立了L2范数中状态、共状态和控制变量的全局后验上界。此外,在两个空间维度(d = 2)的情况下,得到了状态变量和协状态变量误差的局部下界和控制变量误差的全局下界。数值实验结果支持了理论结果。
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来源期刊
Optimal Control Applications & Methods
Optimal Control Applications & Methods 工程技术-应用数学
CiteScore
3.90
自引率
11.10%
发文量
108
审稿时长
3 months
期刊介绍: Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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