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Record W4214848967 · doi:10.1139/tcsme-2021-0008

Adaptive robust control for the corner balancing Cubli system with uncertainties

2022· article· en· W4214848967 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Adaptive controlInertiaRobust controlUnderactuationLyapunov functionLyapunov stabilityComputer scienceMATLABBounded functionRobustness (evolution)Control systemControl (management)EngineeringMathematicsNonlinear systemPhysics

Abstract

fetched live from OpenAlex

The Cubli is a cube that can balance on its edge or corner by rotating inertia wheels. It is a typical underactuated mechanical system, and has 6 degrees of freedom when balancing on its corner. In this paper, an adaptive robust control is presented to balance the uncertain Cubli system on its corner. The uncertainties are considered to be time-varying and bounded, but the bounds are unknown. We first established a dynamic model of the uncertain Cubli system, subject to the servo constraints. Next, we present the robust control with a leakage-type adaptive law. We used the Lyapunov theory to verify the stability of the control. Finally, the effectiveness of the control was verified through numerical simulation in MATLAB™.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.998

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 studies0.0010.000
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 head0.007
GPT teacher head0.159
Teacher spread0.152 · 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