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Application of Sliding Mode Control for the Formation of Heterogeneous Multi-Agent Systems

2021· article· en· W4205668644 on OpenAlex

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

Venue2021 IEEE Conference on Control Technology and Applications (CCTA) · 2021
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMobile robotSliding mode controlControl theory (sociology)Computer scienceScheme (mathematics)Multi-agent systemMode (computer interface)Stability (learning theory)Control (management)RobotControl engineeringTopology (electrical circuits)Distributed computingEngineeringArtificial intelligenceMathematicsNonlinear system

Abstract

fetched live from OpenAlex

This paper proposes a fundamental sliding mode control (SMC) method for the formation of heterogeneous multi-agent systems with fixed topology and a virtual leader consisting of quadrotor unmanned aerial vehicles (UAVs) and two wheeled mobile robot (2WMR) unmanned ground vehicles (UGVs). SMC is used to direct the agents for the purposes of achieving consensus and formation in a two-dimensional environment. The stability analysis with the resultant system shows that the errors can be driven to zero. Finally, simulation results are shown to demonstrate the effectiveness of the proposed control scheme for a team of three UAVs and three UGVs. Both static and dynamic formation cases are validated with simulated studies, as well as a case with a disturbance.

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 score0.992
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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