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Vehicle Platoon String Stability: Network Passivity Approach

2020· article· en· W3091596423 on OpenAlex
Chiedu N. Mokogwu, Keyvan Hashtrudi-Zaad

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2020 IEEE Conference on Control Technology and Applications (CCTA) · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsQueen's University
Fundersnot available
KeywordsPlatoonPassivityInterconnectionControl theory (sociology)Network topologyTopology (electrical circuits)CascadeComputer scienceString (physics)Stability (learning theory)Control engineeringEngineeringMathematicsControl (management)Computer network

Abstract

fetched live from OpenAlex

Control of large interconnected systems with different interconnection topologies has primarily been tackled using decentralized control. An implementation of decentralized control is in string stability of interconnected systems with applications to vehicle following. In this paper, the use of passivity formalism as a means to analyse string stability in vehicle platoons is proposed. In order to employ passivity, network theory is used to model the interconnection topology of the vehicle platoon system. A bidirectional vehicle platoon, modelled by linear dynamics under constant distance spacing, employing linear controllers is used as a case study. With this in mind, we show that any arbitrary length bidirectional platoon can be modelled as a combination of a cascade of two-port networks coupled to a one-port network. Consequently, the stability of the coupled system can be analysed using passivity theory. The work is supported by theoretical and numerical analysis.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.199
Teacher spread0.182 · 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