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Record W2800493559 · doi:10.1139/tcsme-2009-0034

AN AUTOPILOT DESIGN FOR THE LONGITUDINAL DYNAMICS OF A LOW-SPEED EXPERIMENTAL UAV USING TWO-TIME-SCALE CASCADE DECOMPOSITION

2009· article· en· W2800493559 on OpenAlexvenueno aff
Jaw-Kuen Shiau, Der-Ming Ma

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAutopilotCascadeControl theory (sociology)Inner loopDecompositionController (irrigation)Computer scienceLoop (graph theory)Control systemControl engineeringEngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

This paper discusses a cascade decomposition method for two-time-scale systems. We decompose the two-time-scale system into slow and fast subsystems connected in cascade form. With the proposed decomposition method, fast and slow low-order subsystems can be easily extracted from the two-time-scale full order system. The longitudinal dynamics of a low speed experimental UAV is used to illustrate the proposed method. An altitude hold control structure is constructed to allow sequential designs based on the lower order fast and slow models using classical designs. The structure contains two control loops. The inner loop is for altitude rate regulation. In this control loop, we use combination of altitude rate and pitch angle as the feedback signals. A fast controller is designed to satisfy the high frequency requirements using the fast model. Following the results of the fast design, three control gains are designed to satisfy the low frequency requirements using the slow models. The design results of the inner loop are used to design the outer altitude control loop. Frequency domain analysis and computer simulations confirm the success of the proposed decomposition method and verify the effectiveness of the control law using the proposed control structure.

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 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 score0.819
Threshold uncertainty score0.578

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 head0.014
GPT teacher head0.248
Teacher spread0.235 · 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.

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

Citations16
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207