{"title":"P-stable abstractions of hybrid systems","authors":"Anna Becchi, Alessandro Cimatti, Enea Zaffanella","doi":"10.1007/s10270-023-01145-x","DOIUrl":null,"url":null,"abstract":"<p>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 <i>synthesizing</i> <span>\\({\\mathbb {P}}\\)</span>-<i>stable abstractions</i>. Intuitively, the <span>\\({\\mathbb {P}}\\)</span>-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 <span>\\({\\mathbb {P}}\\)</span>. A <span>\\({\\mathbb {P}}\\)</span>-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 <span>\\({\\mathbb {P}}\\)</span>-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 <span>\\({\\mathbb {P}}\\)</span>-stable abstractions for significant systems.</p>","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"37 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software and Systems Modeling","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10270-023-01145-x","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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