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Record W1846452925 · doi:10.1109/icar.1997.620236

Geometric approach to feedback stabilization of a hopping robot in the flight phase

2002· article· en· W1846452925 on OpenAlexaff
Fazal ur Rehman, H. Michalska

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsMcGill University
Fundersnot available
KeywordsControllabilityControl theory (sociology)Robustness (evolution)Lie algebraLie groupTrajectoryExponential stabilityMathematicsMobile robotRobotComputer scienceApplied mathematicsPure mathematicsControl (management)

Abstract

fetched live from OpenAlex

Using a model of a hopping robot it is shown that a previously introduced novel approach for the synthesis of time-varying stabilizing feedback control for drift free systems, which is based on the trajectory intersection idea and primarily applies to systems whose controllability Lie algebra is finite dimensional, is also applicable to systems whose controllability algebra is infinite dimensional. The original model of the hopping robot is first approximated to yield a simplified model whose controllability Lie algebra is finite dimensional. A time varying stabilizing feedback law is then constructed for the simplified model. The latter can be viewed as a composition of a standard stabilizing feedback control for a Lie algebraic extension of the system and a periodic continuation of a parametrized solution to a certain open-loop, finite horizon trajectory interception problem which is stated and solved in logarithmic coordinates of flows. An adequately large stability robustness margin for the extended controlled system can always be insured and is shown to guarantee that the constructed feedback control is also stabilizing for the original model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.213
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2002
Admission routes1
Has abstractyes

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