Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 435

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems

Torrisi, F.D., Bemporad, A.·2004·IEEE Transactions on Control Systems Technology

Abstract

This paper presents a computational framework for modeling hybrid systems in discrete-time. We introduce the class of discrete hybrid automata (DHA) and show its relation with several other existing model paradigms: piecewise affine systems, mixed logical dynamical systems, (extended) linear complementarity systems, min-max-plus-scaling systems. We present HYSDEL (hybrid systems description language), a high-level modeling language for DHA, and a set of tools for translating DHA into any of the former hybrid models. Such a multimodeling capability of HYSDEL is particularly appealing for exploiting a large number of available analysis and synthesis techniques, each one developed for a particular class of hybrid models. An automotive example shows the modeling capabilities of HYSDEL and how the different models allow to use several computational tools.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tcst.2004.824309

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 21 references

Cited by

See all 435

Metadata

Authors
Torrisi, F. D., & Bemporad, A.
Venue
IEEE Transactions on Control Systems Technology
Published
2004-03
Volume
12
Issue
2
Pages
235-249
ISSN
1063-6536 (ISSN-L)
OpenAlex
W2105708535
Cited by
435
References
21
FWCI
25.85
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems. https://app.cochat.ai/papers/10.1109/tcst.2004.824309
The paper
Torrisi, F. D., & Bemporad, A. (2004). HYSDEL—A Tool for Generating Computational Hybrid Models for Analysis and Synthesis Problems. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2004.824309

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.