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Record W6939357163 · doi:10.60692/cc34b-wkm22

Robust Autopilot Design and Hardware-in-the-Loop Simulation for Air to Air Guided Missile

2017· article· en· W6939357163 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

VenueGreater South Information System · 2017
Typearticle
Languageen
FieldEngineering
TopicGuidance and Control Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAutopilotRobustness (evolution)MissileControl theory (sociology)Nonlinear systemRobust controlAerodynamics

Abstract

fetched live from OpenAlex

This paper proposed a robust autopilot design for air to air guided missile and a Hardware-in-the-Loop (HIL) simulation which is based on the derived missile-control transfer functions and the 6DOF simulation model. The introduced autopilot is implemented within the 6DOF simulation to check its robustness against non-modeled dynamics and nonlinearities. The nonlinear 6DOF equations of motions are solved together to obtain the pitch and yaw transfer functions. The missile equations are described in the form of modules programmed within the C++ environments to form the baseline for subsequent design and analysis. Furthermore, a comparison between both our previous work, i.e. classical and robust autopilot, are justified via HIL simulation. The simulation results demonstrated the robustness capability in presence disturbance and noise.

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)0.001
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 score0.762
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.084
GPT teacher head0.242
Teacher spread0.159 · 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