An Overview of Uncertain Control Co-Design Formulations
An Overview of Uncertain Control Co-Design Formulations
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DOI：
10.1115/1.4062753
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
S. Azad;Daniel R. Herber
中科院分区：
文献类型：
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作者：
S. Azad;Daniel R. Herber
本文探讨了各种不确定控制协同设计(UCCD)问题的表述。虽然以前的工作提供了依赖于方法的公式，并且仅限于少数不确定性(通常来自一门学科)，但将UCD有效地应用于真实世界的动态系统需要彻底理解不确定性及其影响如何被捕获。由于第一步是定义感兴趣的UCD问题，本文旨在通过在一般UCD背景下确定可能的不确定性来源，然后正式确定仅通过问题制定来捕捉其影响的方式(不必立即诉诸具体的解决战略)，来解决当前文献的一些局限性。我们首先开发并讨论了一种可以捕获本文中给出的不确定性表示的广义UCCD公式。讨论了目标函数的处理、解析型等式约束的挑战以及不等式约束的各种公式。然后，给出了更特殊的问题公式，如随机期望、随机机会约束、概率稳健、最坏情况稳健、模糊期望值和可能性机会约束UCD公式。讨论了这些公式中的关键概念，以及来自诸如鲁棒和随机控制理论等密切相关领域的见解，并确定了未来的研究方向。
This article explores various uncertain control co-design (UCCD) problem formulations. While previous work offers formulations that are method-dependent and limited to only a handful of uncertainties (often from one discipline), effective application of UCCD to real-world dynamic systems requires a thorough understanding of uncertainties and how their impact can be captured. Since the first step is defining the UCCD problem of interest, this article aims at addressing some of the limitations of the current literature by identifying possible sources of uncertainties in a general UCCD context and then formalizing ways in which their impact is captured through problem formulation alone (without having to immediately resort to specific solution strategies). We first develop and then discuss a generalized UCCD formulation that can capture uncertainty representations presented in this article. Issues such as the treatment of the objective function, the challenge of the analysis-type equality constraints, and various formulations for inequality constraints are discussed. Then, more specialized problem formulations such as stochastic in expectation, stochastic chance-constrained, probabilistic robust, worst-case robust, fuzzy expected value, and possibilistic chance-constrained UCCD formulations are presented. Key concepts from these formulations, along with insights from closely-related fields, such as robust and stochastic control theory, are discussed, and future research directions are identified.