混合系统的 P 稳定抽象

IF 2 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software and Systems Modeling Pub Date : 2024-01-29 DOI:10.1007/s10270-023-01145-x
Anna Becchi, Alessandro Cimatti, Enea Zaffanella
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引用次数: 0

摘要

稳定性是动力学系统的基本要求。大多数研究都集中于验证给定稳定区域的稳定性。在本文中,我们要解决的问题是合成 \({\mathbb {P}}\)-stable 抽象。直观地说,动态系统的 \({\mathbb {P}}\) - 稳定抽象描述了响应外部输入时稳定区域之间的转换。稳定区域并不是给定的,而是根据给定的谓词集 \({\mathbb {P}}\) 合成的最精确的表示。稳定抽象由稳定持续时间的时序信息丰富。我们在 "抽象解释 "框架内实现了一种合成算法,允许不同程度的近似。我们展示了 \({\mathbb {P}}\)-稳定抽象的表征能力,它提供了系统稳定性行为的高层次描述,我们还通过实验评估了该算法在为重要系统综合 \({\mathbb {P}}\)-稳定抽象方面的有效性。
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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P-stable abstractions of hybrid systems

Stability is a fundamental requirement of dynamical systems. Most of the works concentrate on verifying stability for a given stability region. In this paper, we tackle the problem of synthesizing \({\mathbb {P}}\)-stable abstractions. Intuitively, the \({\mathbb {P}}\)-stable abstraction of a dynamical system characterizes the transitions between stability regions in response to external inputs. The stability regions are not given—rather, they are synthesized as their most precise representation with respect to a given set of predicates \({\mathbb {P}}\). A \({\mathbb {P}}\)-stable abstraction is enriched by timing information derived from the duration of stabilization. We implement a synthesis algorithm in the framework of Abstract Interpretation that allows different degrees of approximation. We show the representational power of \({\mathbb {P}}\)-stable abstractions that provide a high-level account of the behavior of the system with respect to stability, and we experimentally evaluate the effectiveness of the algorithm in synthesizing \({\mathbb {P}}\)-stable abstractions for significant systems.

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来源期刊
Software and Systems Modeling
Software and Systems Modeling 工程技术-计算机:软件工程
CiteScore
6.00
自引率
20.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns: Domain-specific models and modeling standards; Model-based testing techniques; Model-based simulation techniques; Formal syntax and semantics of modeling languages such as the UML; Rigorous model-based analysis; Model composition, refinement and transformation; Software Language Engineering; Modeling Languages in Science and Engineering; Language Adaptation and Composition; Metamodeling techniques; Measuring quality of models and languages; Ontological approaches to model engineering; Generating test and code artifacts from models; Model synthesis; Methodology; Model development tool environments; Modeling Cyberphysical Systems; Data intensive modeling; Derivation of explicit models from data; Case studies and experience reports with significant modeling lessons learned; Comparative analyses of modeling languages and techniques; Scientific assessment of modeling practices
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