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Record W2170240441 · doi:10.1109/acc.2005.1470434

Quasipassivity-based robust nonlinear control synthesis for flap positioning using shape memory alloy micro-actuators

2005· article· en· W2170240441 on OpenAlexaff
N. Léchevin, C.A. Rabbath

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsMcGill UniversityUniversité du Québec à Trois-RivièresDefence Research and Development Canada
Fundersnot available
KeywordsControl theory (sociology)ActuatorRobustness (evolution)Nonlinear systemFeed forwardParametric statisticsAerodynamicsSliding mode controlRobust controlCascadeComputer scienceEngineeringControl systemControl engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper proposes a quasipassivity-based robust nonlinear control law for the position control of a rotary flap aimed at stabilizing the free-to-roll motion of an aerial vehicle. An antagonist-type shape memory alloy microactuator (SMA) is used. The plant is modeled as a cascade decomposition comprising a 2nd-order, parametric-uncertain system excited by a bounded exogenous aerodynamic moment and a 1st-order heat conduction system characterized by a hysteretic output. The control objective is to warrant accurate and fast rotation of the flap at an angle determined by the outer-loop controller. The control law is obtained from subsystem decomposition that is suitable for quasipassivation by application of feedback and feedforward control. The cascade control structure is composed of 1) sliding mode control with boundary layer, which robustifies the flap positioning, and 2) PD control that compensates for the delay induced by the hysteretic characteristics of the SMA. The control scheme provides ultimate boundedness (UB) of tracking error trajectories and robustness to uncertain, bounded, stiffness constant and aerodynamic moment. Simulations validate the proposed approach.

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 categoriesMeta-epidemiology (narrow)
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 score0.688
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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.

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

Citations13
Published2005
Admission routes1
Has abstractyes

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