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Record W299710491 · doi:10.1139/tcsme-2007-0012

ON UNIQUENESS OF FILIPPOV’S SOLUTIONS FOR NON-SMOOTH SYSTEMS HAVING MULTIPLE DISCONTINUITY SURFACES WITH APPLICATIONS TO ROBOTIC CONTROL SYSTEMS

2007· article· en· W299710491 on OpenAlexaffvenue
Q. Wu, Hongwei Zeng

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDiscontinuity (linguistics)UniquenessIntersection (aeronautics)TangentMathematicsSurface (topology)Control theory (sociology)Computer scienceMathematical analysisControl (management)GeometryArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Analysis of the uniqueness of Filippov's solutions to non-smooth robotic control systems is important before the solutions can be sought. Such an analysis is extremely challenging when the discontinuity surface is the intersection of multiple discontinuity surfaces. The key step is to study the intersections of the convex sets from Filippov's inclusions and their associated sets containing vectors tangent to the discontinuity surfaces. For practical non-smooth robotic systems, due to their complexities, the determination of the intersections of these sets symbolically is extremely difficult if not impossible. In this paper, we propose a method such that the determinations of the intersections become feasible. Two examples of practical non-smooth robotic control systems are presented to demonstrate the efficacy of the method. The work contributes significantly to the analysis of non-smooth systems where the proof of the uniqueness of Filippov’s solution is crucial to keep the mathematical model relevant to physical systems and to ensure the numerical solutions can be sought.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes2
Has abstractyes

Explore more

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