通过惩罚函数解决具有常规混合约束条件的最优控制问题

IF 1.6 3区 数学 Q2 MATHEMATICS, APPLIED
Maria do Rosário de Pinho, Maria Margarida A. Ferreira, Georgi Smirnov
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引用次数: 0

摘要

下面,我们将利用惩罚函数方法,推导出具有混合约束条件问题的最优性必要条件(参见 Dmitruk 在 Control Cybern 38(4A):923-957, 2009 中的文章),该方法与我们之前用于解决控制扫频过程优化问题的方法类似(参见 De Pinho 等人在 Optimization 71(11A):3363-3381, 2022 中的文章)、De Pinho 等人,载于《优化》71(11):3363-3381, 2022 年),以及最近用于解决纯状态约束的最优控制问题的方法(见 De Pinho 等人,载于《Syst Control Lett》188:105816, 2024 年)。我们有意考虑平稳情况和最简单的边界条件;我们考虑全局最小值,并假设控制系统的轨迹集是紧凑的。基于我们的惩罚函数方法,我们开发了一种数值方法,允许对达到给定精度所需的参数进行估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimal Control Problem with Regular Mixed Constraints via Penalty Functions

Optimal Control Problem with Regular Mixed Constraints via Penalty Functions

Below we derive necessary conditions of optimality for problems with mixed constraints (see Dmitruk in Control Cybern 38(4A):923–957, 2009) using the method of penalty functions similar to the one we previously used to solve optimization problems for control sweeping processes (see, e.g., De Pinho et al. in Optimization 71(11):3363–3381, 2022) and, more recently, to solve optimal control problems with pure state constraints (see De Pinho et al. in Syst Control Lett 188:105816, 2024). We intentionally consider a smooth case and the simplest boundary conditions; we consider global minimum and assume that the set of trajectories of the control system is compact. Based on our penalty functions approach we develop a numerical method admitting estimates for its parameters needed to achieve a given precision.

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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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